Deep - Learning - Based Automatic Segmentation and Quantitative Measurement of Normal Spleen in Chinese Adults

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Additionally, the study intends to examine age-related changes in the normal spleen's volume as well as the enhancement characteristics of the normal spleen on contrast-enhanced CT scans. Materials and Methods： A total of 2856 images of spleens(dataset 1) were collected from four public sources and randomly split into three groups: training (2292 images), validation (280 images), and test (284 images). The segmentation efficiency for the spleen was evaluated by the Dice similarity coefficient (DSC), volume similarity (VS), Hausdorff distance (HD), and average HD. Another dataset of 3490 normal spleen CT images (dataset 2) was obtained for external validation, including 862 non-contrast images, 744 arterial phase images, 947 portal venous phase images, and 937 delayed phase images. Then, 947 portal venous phase image series of inpatient contrast-enhanced abdominal CT examinations with normal spleen were included for analysis and grouped by every 10-year gap. A 3D U-Net-based segmentation model was used to predict spleen labels, followed by manual label modifications as appropriate. Quantitative parameters of the spleen (volume, CT value, and diameter) were then analyzed. Results： In dataset 1, the testing dataset (N = 284) showed segmentation performance with a Dice Similarity Coefficient (DSC) of 0.982 [0.975–0.988], Volume Similarity (VS) of 0.995 [0.991,0.998] , Hausdorff Distance (HD) of 3.047 [2.578, 4.653] mm, and Average HD of 0.014 [0.009, 0.019] mm. In dataset 2, the distribution ranges of the three-dimensional diameters (x, y, z) of the spleen were as follows: for males, 9.20 [8.40 - 9.95] cm (median [interquartile range]), 9.50 ± 1.97 cm (mean ± standard deviation), and 9.40 ± 1.91 cm; for females, 9.50 ± 1.09 cm, 8.92 ± 1.83 cm, and 8.64 ± 1.67 cm. The distribution range of the spleen volume was 213.74 [158.4, 284.88] cm³ for males and 163.7 [125.99, 217.72] cm³ for females. In the enhanced scan images of the spleen, it was found that the CT values of the spleen in the arterial phase, portal venous phase, and delayed phase were all higher in females than in males. With the increase in age, the spleen volume of males showed a trend of first increasing and then decreasing, reaching its peak at the age of 28–37, with a peak value of 273.75 ± 92.96 cm³. The spleen volume of females gradually decreased with the increase of age, with the peak value of 225.01 ± 65.53 cm³. In all age groups, the spleen volume of females was smaller than that of males. Conclusion： The spleen segmentation tool based on deep learning can segment the spleen on CT images and measure its normal diameter, volume, and CT value accurately and effectively. Spleen morphometric deep learning computed tomography Figures Figure 1 Figure 2 Figure 3 1. Introduction The spleen, the biggest immune organ in the human body, is situated in the left hypochondrium and is essential for immunological functions and blood filtration [1]. Studies have demonstrated a significant association between changes in spleen volume and inflammatory, infectious, metabolic, and hematological disorders [2–6]. Consequently, monitoring its fluctuations is essential for assessing prognosis in relevant populations. Variations in spleen volume can be utilized to monitor treatment efficacy and predict prognosis in patients undergoing immunotherapy for metastatic tumors [3], maintenance chemotherapy for malignant tumors [4], and transcatheter arterial chemoembolization for hepatocellular carcinoma [5]. Measuring spleen volume aids in categorizing disease risk in individuals with uremia undergoing peritoneal dialysis [6], severe liver fibrosis [7], and esophageal variceal hemorrhage [8]. Consequently, precise evaluation of spleen volume is crucial for diagnosis and therapeutic decisions. CT images distinctly reveal diffuse or localized irregularities in splenic density. Volume measures derived from CT images correspond with actual values [9, 10]. In actual situations, radiologists infrequently quantify spleen volume accurately. They evaluate spleen enlargement by ascertaining if it surpasses five rib units in cross-sectional images. This results in clinical evaluations that are predominantly qualitative. This qualitative analytical method may result in overlooking small changes in spleen volume, thereby reducing the accuracy of clinical evaluations. As artificial intelligence (AI) advances in the field of medical imaging, many researchers have used AI segmentation models to perform structural segmentation of solid organs such as the pancreas, prostate, kidneys, liver, adrenal glands, and musculoskeletal system [11–15], as well as to automatically output quantitative parameters such as organ structure dimensions, density, and volume. This provides quantitative data to the qualitative diagnosis. A spleen segmentation model based on a deep learning network can be developed and used for automatic measurements, such as density, volume, and diameters, aiding in the efficient and accurate quantitative description of the spleen that has the potential to promote the diagnosis of spleen diseases. Moreover, such a segmentation model laid the root for further spleen classification and focal lesion segmentation. Previous studies have shown the age-related distribution of spleen volume [16–18]. These morphological studies did not utilize the automated segmentation process based on deep learning. Presently, an increasing number of studies utilize spleen volume as a prognostic indicator for certain illness populations, but there is a deficiency of research applying 3D U-Net-based segmentation to segment the spleen in a large cohort of healthy individuals and to assess the resulting data. In this study, we established a 3D U-Net-based model for the automatic segmentation of spleens in abdominal CT images and tried to discover the changes in morphometrics of normal spleens in different age groups. 2. Materials and methods 2.1. patients This retrospective study was approved by the institutional review board of Peking University First Hospital [No. 2024 (222-004)], with a waiver of informed consent. 2.2. Training dataset for a 3D U-Net segmentation model A total of 2,856 image datasets were collected from the publicly available datasets Abdomen_CT_1k( https://abdomenct-1k-fully-supervised-learning.grand-challenge.org/),Flare23(https://codalab.lisn.upsaclay.fr/competitions/12239#learn_the_details-dataset ), AMOS( https://zenodo.org/record/7262581#.ZFNB6nZByUk ), and RSNA( https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data ), which were subsequently utilized to train the model. The training dataset was randomly divided into training (n = 2292), validation (n = 280), and test (n = 284) sets for the purpose of training the 3D U-Net segmentation model. This specific dataset wasn't used for quantitative analysis of the spleen. 2.3. Validation dataset for segmentation model and morphometric analysis of spleen We collected patients who underwent abdominal CT examinations who were registered in our institution from January 2023 to December 2023, including both non-contrast computed tomography (NCCT) and contrast-enhanced computed tomography (CECT) examinations. The CT images were performed on four CT scanners in our hospital: Siemens SOMATOM Definition Flash CT (Siemens Healthcare), GE LightSpeed VCT (GE Healthcare), Philips Brilliance 256 iCT scan (Philips Healthcare), and GE Discovery CT750HD (GE Healthcare). The imaging parameters were as follows:slice thickness 1.25 mm and 1 mm. The patients were considered to have a “normal” spleen according to comprehensive clinical and imaging information. The inclusion criteria were as follows: (1) age ≥ 18 years; (2) images including the entire spleen; (3) medical imaging reports of a normal spleen; (4) no clinical history of spleen-related diseases, such as hematological disorders: hypersplenism, various anemias (aplastic, hemolytic, hereditary spherocytosis), leukemia (acute or chronic myeloid), lymphoma, macroglobulinemia, myelofibrosis; hepatic disorders: liver cirrhosis, portal hypertension, hepatitis, liver space-occupying lesions; neoplastic conditions: liver cancer, pancreatic cancer, pancreatic space-occupying lesions, or maintenance chemotherapy for malignancies; (5) there is no clinical history of an operation on the spleen. We also applied the following exclusion criteria: (1) Image quality defects include incomplete acquisition of examination images, insufficient scanning range that fails to cover the entire spleen, and respiratory motion artifacts that hinder accurate diagnosis; (2) anatomical abnormalities: postoperative absence of the spleen and significant deformity; (3) imaging abnormalities: re-evaluation of the scans indicates abnormal structure and density of the spleen, as well as suspected lesions existing in the spleen. 2.4. Model training This study was based on a 3D U-Net architecture equipped with Nvidia Tesla P100 16G (Nvidia Corporation, Santa Clara, CA) GPU and PyTorch v1.7.1 + cu110 ( https://pytorch.org/ ). The model inputs CT images and outputs spleen volume, average CT values, and three-dimensional diameters. Image preprocessing included window adjustment (center 30 HU, width 300 HU), resizing to 128 px × 192 px × 256 px, and image augmentation by rotating, sheering, noise injection, denoising, etc. The training parameters were batch size 6, learning rate 0.0001, and epoch 400. 2.5. Evaluation of the segmentation model Objective and subjective evaluations assessed the spleen segmentation model's performance. Dice similarity coefficient (DSC), volume similarity (VS), Hausdorff distance (HD), and average HD of model training and test sets were objective evaluation approaches. Two imaging radiologists also subjectively assessed the external verification data segmentation prediction structure. Inclusion of all spleen structures was evaluated. Whether elements outside the spleen (such as the splenic artery, vein, diaphragm, stomach wall, and intestinal tract) were included, the segmentation effect was unsatisfactory if the missing range of the spleen label surpassed 5% of its entire volume. The segmentation effect was insufficient if the spleen label extended outside the spleen and reached 5% of its volume. Images with poor segmentation were manually modified, and the final result was output. 2.6. Quantitative measurements The image series in the external validation data underwent autolabeling by the spleen segmentation model. These spleen labels were checked by a junior radiologist and a senior radiologist together, and unsatisfied labels were modified manually. Necessary manual modification of labels was performed to ensure that the labelled area (as a region of interest [ROI]) contained the spleen only (Fig. 2 (a)-(d)), and adjacent structures, such as the gastric wall, kidney, or liver, were not covered. Labels that exceeded or missed 5% of spleen volume were also modified. The volume, average CT values, and three-dimensional diameters of the ROIs were measured. 2.7. Statistical analysis The statistical analysis was performed in R 4.4.2. All statistical significance values were set at P < 0.05. The Shapiro-Wilk test determined data normality. If the Shapiro-Wilk test p-value was larger than 0.05, the t-test was employed for inter-group comparisons; otherwise, the two-sample Wilcoxon test was used. ANOVA was employed in multi-group comparisons if the data all corresponded to a normal distribution and passed the homogeneity of variance test; otherwise, the Kruskal-Wallis test was used. 3. Results 3.1. Spleen segmentation model evaluation The model's quantitative results are presented in Table 1. In dataset 1, the testing dataset (N = 284) demonstrated segmentation performance with a Dice Similarity Coefficient (DSC) of 0.982 [0.975–0.988], Volume Similarity (VS) of 0.995 [0.991, 0.998], Hausdorff Distance (HD) of 3.047 [2.578, 4.653] mm, and Average HD of 0.014 [0.009, 0.019] mm. In dataset 2, two radiologists assessed the segmentation predictions of the algorithm. Photos depicting focal or diffuse lesions in the spleen were removed, and photos with inadequate segmentation findings were manually adjusted. Ultimately, segmentation outcomes from 3,490 pictures were accessible for precise computation of spleen volume. 3.2. Morphometric analysis of spleen As shown in Table 1, the average CT values of the male spleen in the non-contrast (NoC), arterial phase (AP), portal venous phase (PVP), and delayed phase (DP) were 43.86 [40.37, 46.93]. HU, 104.17 ± 21.42 HU, 106.34 [97.71, 116.64] HU, 82.35 [75.18, 91.01] HU, respectively; average CT values of the female spleen in each enhancement phase were 43.43 [40.38, 46.8] HU, 120.72 [105.47, 135.33] HU, 114.82 [105.47, 125.11] HU, 86.73 [78.99, 96.22] HU, respectively. Analysis using the two-sample Wilcoxon test revealed that the average CT values of the female spleen throughout the arterial phase, portal venous phase, and delayed phase were all significantly greater than those of the male, with all differences being statistically significant (P < 0.001) (Fig. 3(a)). Table 3 indicates that the volume distribution range of the male spleen (N = 506) was 213.74 [158.4, 284.88] cm³, whereas the volume distribution range of the female spleen was 163.7 [125.99, 217.72] cm³. In each age group, the spleen volume of male patients exceeded that of female patients. With the exception of the 18-27-year-old cohort, no statistically significant difference in spleen volume was observed between males and females (T-test, t = 1.38, P = 0.19). However, significant statistical differences in spleen volume between genders were noted in the remaining age groups (P values < 0.001). As aging progresses, the spleen volume in male patients exhibits an initial increase followed by a subsequent decrease, peaking between 28 and 37 years, with a maximum value of 273.75 ± 92.96 cm³. The variation in female spleen volume with age exhibited a bimodal distribution, attaining its maximum from 18 to 27 years, with a peak value of 225.01 ± 65.53 cm³ (Fig. 3(b)). As age advances, the elevated CT value of the spleen exhibited a pattern of initial decline followed by subsequent increase (Figure 3(c)). The three-dimensional dimensions (x × y × z) of the spleen for all included patients were 9.2 [8.4, 9.95] cm × 9.5 ± 1.97 cm × 9.4 ± 1.91 cm for males and 8.45 ± 1.09 cm × 8.92 ± 1.83 cm × 8.64 ± 1.67 cm for females, respectively (Figures 3(d)–(f)). Table 1 Objective evaluation results of the spleen segmentation model parameter External validation set Testing set Training set validation set Statistical value P value Count n=3490 n=284 n=2292 n=280 Dice similarity coefficient 0.736～1 0.982[0.975,0.988] 0.983[0.977,0.987] 0.982[0.974,0.987] 5393.048 <0.001 Volume similarity 0.738～1 0.995[0.991,0.998] 0.996[0.992,0.998] 0.996[0.991,0.998] 5313.516 <0.001 Hausdorff distance /mm 0～57.237 3.047[2.578,4.653] 2.839[2.389,3.658] 3.094[2.528,4.713] 5263.445 <0.001 Average Hausdorff distance/mm 0～21.963 0.014[0.009,0.019] 0.013[0.009,0.017] 0.014[0.01,0.021] 5317.603 <0.001 Data are described as median [interquartile range] or minimum ~ maximum. Table 2 Quantitative parameters of spleen in different phase All Noc Male Female Statistical value P value Male Female Statistical value P value Count N=1939 N=1551 N=473 N=389 Average CT value/HU 88.52[61.56,105.09] 96.91[59.04,116.57] 1297301 <0.001 43.86[40.37,46.93] 43.43[40.38,46.8] 96145 0.25 Volume/cm3 210.42[160.55,276.41] 161.68[125.3,216.02] 2018213 <0.001 206.32[157.23,274.71] 157.55[122.56,209.96] 123943 <0.001 Diameters/cm x 9.18[8.40,9.95] 8.54[7.82,9.22] 1999402 <0.001 9.15±1.18 8.48±1.08 8.74 <0.001 y 9.37[8.07,10.73] 8.82[7.58,10.12] 1777732 <0.001 9.31±1.99 8.75±1.89 4.23 <0.001 z 9.40[8.05,10.70] 8.50[7.40,9.70] 1895192.5 <0.001 9.31±1.92 8.40[7.40,9.30] 119674 <0.001 Data conforming to a normal distribution are expressed as mean ± standard deviation, otherwise as median [interquartile range]. Table 2 Quantitative parameters of spleen in different phase(continued Table) AP PVP DP Male Female Statistical value P value Male Female Statistical value P value Male Female Statistical value P value N=427 N=317 N=506 N=441 N=533 N=404 104.17±21.42 120.72[105.47,135.33] 39127 <0.001 106.34[97.71,116.64] 114.82[105.47,125.11] 76560 <0.001 82.35[75.18,91.01] 86.73[78.99,96.22] 84697 <0.001 204.18[157.5,270.18] 158.87[124.47,212.02] 91044 <0.001 213.74[158.4,284.88] 163.7[125.99,217.72] 149411.5 <0.001 215.14[169.59,278.78] 167.94[127.98,226.22] 143889 <0.001 9.08±1.18 8.59[7.8,9.23] 86483.5 <0.001 9.2[8.4,9.95] 8.45±1.09 151433 <0.001 9.27±1.12 8.65[7.93,9.28] 143818.5 <0.001 9.34[8.21,10.67] 8.78±1.81 82694 <0.001 9.5±1.97 8.92±1.83 4.65 <0.001 9.51±1.9 8.86±1.89 5.13 <0.001 9.44±1.95 8.50[7.50,9.50] 85111.5 <0.001 9.40±1.91 8.64±1.67 6.55 <0.001 9.50[8.10,10.80] 8.57[7.60,9.80] 134317.5 <0.001 Table 3 Quantitative parameters of spleen in different age groups All 18-27 28-37 Male Female Statistical value P value Male Female Statistical value P value Male Female Statistical value P value Count N=506 N=441 N=9 N=20 N=16 N=34 Average CT value/HU 106.34[97.71,116.64] 114.82[105.47,125.11] 76560 <0.001 118.16±12.8 122.08±17.34 -0.68 0.5 103.38±10.92 110.87[105.07,124.5] 142 0.01 Volume/cm3 213.74[158.40,284.88] 163.70[125.99,217.72] 149411.5 <0.001 267.56±81.17 225.01±65.53 1.38 0.19 273.75±92.96 183.33±63.42 3.52 0 Diameters/cm x 9.20[8.04,9.95] 8.45±1.09 151433 <0.001 9.12±1.04 8.76±1.26 0.8 0.44 9.52±1.1 8.55±1.11 2.89 0.01 y 9.5±1.97 8.92±1.83 4.65 <0.001 10.71±1.29 10.16±1.62 0.99 0.34 10.89±1.8 9.3±1.81 2.92 0.01 z 9.40±1.91 8.64±1.67 6.55 <0.001 10.28±1.65 9.53±1.34 1.2 0.25 10.46±1.77 8.94±1.41 3 0.01 Data conforming to a normal distribution are expressed as mean ± standard deviation, otherwise as median [interquartile range]. Table 3 Quantitative parameters of spleen in different age groups(continued Table) 38-47 48-57 58-67 Male Female Statistical value P value Male Female Statistical value P value Male Female Statistical value P value N=27 N=38 N=74 N=77 N=165 N=116 106.22±14.01 113.12±16.2 -1.83 0.07 106.05[97.68,114.95] 113.35±14.72 2086 <0.001 103.96[95.88,112.71] 114.51[105.79,124.28] 5568 <0.001 248.97±62.16 184.15[140.2,233.26] 762 <0.001 247[192.50,322.56] 198.48[153.55,252.44] 3848 <0.001 217.04[159.9,288.69] 162.5[127.18,218.46] 12938 <0.001 9.44±1.05 8.49±1.01 3.65 <0.001 9.07[8.34,10.00] 8.72±1.04 3471 0.02 9.13±1.15 8.44±1.06 5.22 <0.001 9.87±1.41 9.60±1.69 0.7 0.49 10.04±1.52 9.23[8.36,10.56] 3390 0.04 9.63±2.13 8.78±1.75 3.68 <0.001 10.30±1.57 8.95±1.59 3.41 <0.001 10.39±1.69 9.43±1.84 3.33 <0.001 9.45±1.82 8.70±1.51 3.76 <0.001 Table 3 Quantitative parameters of spleen in different age groups(continued Table) 68-77 ≥ 78 Male Female Statistical value P value Male Female Statistical value P value N=156 N=107 N=59 N=49 107.97±14.06 115.48±12.62 -4.52 <0.001 117.41±21.06 123.29±19.72 -1.5 0.14 188.18[141.21,255.14] 141.23[102.34,170.86] 11992 <0.001 171.27[129.04,228.33] 143.15±50.09 1944 0 9.18±1.07 8.27±1.08 6.77 <0.001 8.98±1.08 8.2±1.15 3.63 <0.001 9.07±1.89 8.28±1.63 3.58 <0.001 8.57[7.56,9.62] 8.31±1.72 1585.5 0.39 8.95±1.89 8.04±1.53 4.28 <0.001 8.42±1.83 7.76±1.56 2.03 0.05 4. Discussion Quantitative imaging refers to the extraction of measurable characteristics from medical pictures. These features are utilized to evaluate the severity, extent of alteration, or condition status in comparison to normalcy versus sickness, damage, or chronic conditions [19]. Compared to traditional visual assessment, quantitative imaging offers a more objective and accurate dataset for evaluation while enabling dynamic analysis that aids in monitoring subtle changes during longitudinal follow-up (e.g., the yearly growth rate of spleen volume). Recent years have shown substantial progress in medical image segmentation approaches utilizing the U-Net architecture. Multiple studies have documented the practicality and therapeutic use of applying 3D U-Net for organ segmentation [20–22]. The 3D U-Net model employed in this study enables accurate segmentation of the spleen in CT images and rapid acquisition of quantitative metrics, including volume (cm³), average CT value (HU), and three-dimensional dimensions (cm). We expect these measurement results to enhance clinical decision-making, specifically in portal hypertension grading and cancer staging. The model exhibited consistent performance throughout the training set (n = 2292), tuning set (n = 280), and test set (n = 284), signifying robust generalization capabilities. Given the model's outstanding segmentation performance on the training dataset, its applicability in more intricate clinical circumstances is a reasonable suggestion. This study developed a spleen segmentation model for thin-slice CT images utilizing the 3D U-Net architecture, capable of performing automatic segmentation of spleens with a substantial sample size. This study selected thin-slice images from the portal venous phase (PVP) for statistical analysis of spleen volume, given the relatively high organ display contrast, extensive scanning range, and routine thin-slice reconstruction in this phase. The cohort exhibiting adequate segmentation effects underwent an analysis of the relationship between spleen volume and age. The results indicated that male spleen volume exhibited an initial increase followed by a fall, whereas female spleen volume initially reduced, then climbed, and subsequently declined after reaching a high. Prior research indicated a trend of decreasing spleen volume with advancing age [16–18, 8]. In contrast, this study revealed that the male spleen volume exhibited an inverted U-shaped trajectory with age, whereas the female spleen volume demonstrated a bimodal distribution with age. The findings may indicate that hormonal regulation governs spleen volume. The hormonal variations preceding and following menopause in women may elucidate the sub-peak phenomena, whereas the comparatively elevated basal metabolic rate in young and middle-aged men may facilitate the compensatory hypertrophy of the spleen [23]. Historically, several study publications have addressed spleen volume, predominantly utilizing data from other nations. Currently, no research has been undertaken on the normative range of spleen volume in the Chinese patient population. Geraghty and colleagues [16] discovered that the mean spleen capacity was 209 cm³ (N = 420), predominantly sourced from the North American population. Ardene Harris and colleagues [17] reported that the mean spleen volume in the Japanese population was 127.4 cm³, with a standard deviation of 62.9 cm³ (N = 230). A separate study [18] determined that the mean spleen volume in the Japanese population was 123 cm³, with a standard deviation of 45 cm³ (N = 238). The researchers established the spleen volume ranges provided above based on data collected from each of their respective locations. The spleen volumes across different groups, as shown in several studies, exhibit significant variation, maybe attributable to ethnic variances. This variation is detectable. This study established that the spleen capacity of adult Chinese males was 213.74 [158.4, 284.88] cm³, whereas that of females was 163.7 [125.99, 217.72] cm³. This study, the inaugural large-sample investigation (N = 1520) of the Chinese population, establishes reference values for spleen volume within this demographic. This study possesses multiple limitations that require elucidation. Initially, while patients with conditions that distinctly influence spleen volume have been omitted from the validation dataset, the remaining population may possess potential confounding variables, including undetected subclinical infections and metabolic irregularities, which could distort the measurements of spleen volume. Secondly, the study failed to thoroughly examine the impact of anthropometric characteristics, including height and weight, on spleen volume. Nevertheless, current research indicates that these elements are intricately associated with spleen development. In future determinations of standard reference intervals, variables such as body surface area and body mass index (BMI) must be incorporated for multifactorial analysis. Furthermore, the existing model is predicated on individuals exhibiting normal spleen morphology, and its applicability to anatomical deviations (such as the spleen-liver wrapping phenomena in patients with beaver tail liver) and clinical conditions is constrained. It is essential to enhance the algorithm's robustness by incorporating intricate scenarios. Single-center studies are susceptible to selection bias at the data level, and the uneven age distribution may influence the generalizability of the findings. Further multicenter and multiethnic large-sample studies must be conducted to enhance generalizability.It is important to acknowledge that while the observed gender disparity in spleen volume is statistically significant, its biological explanation requires more investigation in conjunction with hormone levels, body composition studies, and other factors.The identified limitations indicate that the clinical application of this study's conclusions should be approached with caution; however, they also highlight avenues for optimization in future research, such as enhancing sample diversity, incorporating multimodal data, and reinforcing algorithmic validation in complex scenarios. 5. Conclusion In conclusion, the spleen segmentation model developed using 3D U-Net has demonstrated commendable segmentation efficacy. The ongoing advancement of technology and the incorporation of automated technologies into the workplace are anticipated to facilitate the precise volumetric evaluation of the spleen in clinical practice. Declarations Declaration of No Funding I hereby declare that this study's research has not received any specific financial support from the public, commercial or non-profit sectors. No forms of financial aid, such as scientific research funds from government agencies, corporate sponsorships or grants from charitable organisations, have been obtained during the conduct of this study. All the resources required for the research, including experimental materials, equipment usage and personnel input, have been obtained through non-financial support channels, such as the institution's routine resource allocation, personal efforts and academic cooperation. I am responsible for the accuracy of the above declaration. Should any of the above information prove to be false, I am willing to accept the corresponding academic and legal consequences. Author Contribution Author Contributions Statement:T.T.: Data collection, image evaluation, article writing, statistical analysis and visualisation.- Z.X., Y.Z., X.Z.: Model training and data management.- X.W.: Project planning, management and guidance; article revision.All authors reviewed the manuscript. References G. M. Crane, Y.-C. Liu, and A. Chadburn, “Spleen: Development, anatomy and reactive lymphoid proliferations,” Semin. Diagn. Pathol. , vol. 38, no. 2, pp. 112–124, Mar. 2021, doi: 10.1053/j.semdp.2020.06.003. F. Robertson, P. Leander, and O. Ekberg, “Radiology of the spleen,” Eur. Radiol. , vol. 11, no. 1, pp. 80–95, 2001, doi: 10.1007/s003300000528. V. 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Du, “Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures,” BioMed Res. Int. , vol. 2021, pp. 1–11, Dec. 2021, doi: 10.1155/2021/9956983. Y. Chen, J. Yang, Y. Zhang, Y. Sun, X. Zhang, and X. Wang, “Age-related morphometrics of normal adrenal glands based on deep learning-aided segmentation,” Heliyon , vol. 9, no. 6, p. e16810, June 2023, doi: 10.1016/j.heliyon.2023.e16810. Y. Zhu et al. , “Fully automatic segmentation on prostate MR images based on cascaded fully convolution network,” J. Magn. Reson. Imaging , vol. 49, no. 4, pp. 1149–1156, Apr. 2019, doi: 10.1002/jmri.26337. J. Cai et al. , “Automatic quantitative evaluation of normal pancreas based on deep learning in a Chinese adult population,” Abdom. Radiol. N. Y. , vol. 47, no. 3, pp. 1082–1090, Mar. 2022, doi: 10.1007/s00261-021-03327-x. Z. Sun et al. , “Quantitative evaluation of chronically obstructed kidneys from noncontrast computed tomography based on deep learning,” Eur. J. Radiol. , vol. 136, p. 109535, Mar. 2021, doi: 10.1016/j.ejrad.2021.109535. J. Kaneko, Y. Sugawara, Y. Matsui, T. Ohkubo, and M. Makuuchi, “Normal splenic volume in adults by computed tomography,” Hepatogastroenterology. , vol. 49, no. 48, pp. 1726–1727, 2002. A. Harris et al. , “Splenic volume measurements on computed tomography utilizing automatically contouring software and its relationship with age, gender, and anthropometric parameters,” Eur. J. Radiol. , vol. 75, no. 1, pp. e97–e101, July 2010, doi: 10.1016/j.ejrad.2009.08.013. J. Kaneko, Y. Sugawara, Y. Matsui, and M. Makuuchi, “Spleen size of live donors for liver transplantation,” Surg. Radiol. Anat. SRA , vol. 30, no. 6, pp. 515–518, Aug. 2008, doi: 10.1007/s00276-008-0364-z. A. B. Rosenkrantz et al. , “Clinical Utility of Quantitative Imaging,” 2016. C. E. Cardenas et al. , “Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks,” Phys. Med. Biol. , vol. 63, no. 21, p. 215026, Nov. 2018, doi: 10.1088/1361-6560/aae8a9. G. Litjens et al. , “A survey on deep learning in medical image analysis,” Med. Image Anal. , vol. 42, pp. 60–88, Dec. 2017, doi: 10.1016/j.media.2017.07.005. S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image Segmentation Using Deep Learning: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 44, no. 7, pp. 3523–3542, July 2022, doi: 10.1109/TPAMI.2021.3059968. J. P. Mackern-Oberti, E. L. Jara, C. A. Riedel, and A. M. Kalergis, “Hormonal Modulation of Dendritic Cells Differentiation, Maturation and Function: Implications for the Initiation and Progress of Systemic Autoimmunity,” Arch. Immunol. Ther. Exp. (Warsz.) , vol. 65, no. 2, pp. 123–136, Apr. 2017, doi: 10.1007/s00005-016-0418-6. Additional Declarations No competing interests reported. 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18:47:02","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84686,"visible":true,"origin":"","legend":"","description":"","filename":"99bee42cc773413bb0d203ae28a0996b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7584704/v1/6f07de06daf737bf7862476f.xml"},{"id":94546882,"identity":"291ea492-6777-457f-95d1-81756ef23e09","added_by":"auto","created_at":"2025-10-28 17:41:21","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91883,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7584704/v1/894a9e267d473e1a3e44d093.html"},{"id":94546880,"identity":"708fbe0b-cd56-4ddb-a8c6-8e53dbd1e156","added_by":"auto","created_at":"2025-10-28 17:41:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":198479,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates the image data enrollment and usage\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7584704/v1/8022cd9301868ccc78d67743.png"},{"id":94546974,"identity":"e7c67b50-3edb-4e7c-b28a-40ae2fafed5a","added_by":"auto","created_at":"2025-10-28 17:41:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":282900,"visible":true,"origin":"","legend":"\u003cp\u003eThe spleen was labelled on the venous phase CT image. A schematic illustration of satisfactory spleen segmentation results is presented, where the red area indicates the annotated region. It is clear that Figures a, b, and c, respectively, show schematic depictions of spleen labels in the axial, coronal, and sagittal planes. As shown in Figure 2d, the minimum volume bounding box algorithm is utilized. Here, the length, width, and height of the rectangle are in accordance with the three-dimensional dimensions of the spleen.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7584704/v1/538616786e939d6d3780eb66.png"},{"id":94546603,"identity":"9f2b9943-0e82-44cb-b708-48e99d1f2ff7","added_by":"auto","created_at":"2025-10-28 17:39:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":209476,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in CT value (a) in different phases.Changes in volume (b), CT value (portal venous phase) (c), and diameters (d, e, f) in the spleen in different age groups.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7584704/v1/42b9ade3f507b19cbb4c21fb.png"},{"id":97520854,"identity":"680f0e17-350c-4a52-898e-74278b34660f","added_by":"auto","created_at":"2025-12-05 11:08:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1387623,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7584704/v1/0aa659cd-56da-45e4-a786-d72f4a5d005c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep - Learning - Based Automatic Segmentation and Quantitative Measurement of Normal Spleen in Chinese Adults","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe spleen, the biggest immune organ in the human body, is situated in the left hypochondrium and is essential for immunological functions and blood filtration [1]. Studies have demonstrated a significant association between changes in spleen volume and inflammatory, infectious, metabolic, and hematological disorders [2\u0026ndash;6]. Consequently, monitoring its fluctuations is essential for assessing prognosis in relevant populations. Variations in spleen volume can be utilized to monitor treatment efficacy and predict prognosis in patients undergoing immunotherapy for metastatic tumors [3], maintenance chemotherapy for malignant tumors [4], and transcatheter arterial chemoembolization for hepatocellular carcinoma [5]. Measuring spleen volume aids in categorizing disease risk in individuals with uremia undergoing peritoneal dialysis [6], severe liver fibrosis [7], and esophageal variceal hemorrhage [8]. Consequently, precise evaluation of spleen volume is crucial for diagnosis and therapeutic decisions.\u003c/p\u003e\u003cp\u003eCT images distinctly reveal diffuse or localized irregularities in splenic density. Volume measures derived from CT images correspond with actual values [9, 10]. In actual situations, radiologists infrequently quantify spleen volume accurately. They evaluate spleen enlargement by ascertaining if it surpasses five rib units in cross-sectional images. This results in clinical evaluations that are predominantly qualitative. This qualitative analytical method may result in overlooking small changes in spleen volume, thereby reducing the accuracy of clinical evaluations.\u003c/p\u003e\u003cp\u003eAs artificial intelligence (AI) advances in the field of medical imaging, many researchers have used AI segmentation models to perform structural segmentation of solid organs such as the pancreas, prostate, kidneys, liver, adrenal glands, and musculoskeletal system [11\u0026ndash;15], as well as to automatically output quantitative parameters such as organ structure dimensions, density, and volume. This provides quantitative data to the qualitative diagnosis. A spleen segmentation model based on a deep learning network can be developed and used for automatic measurements, such as density, volume, and diameters, aiding in the efficient and accurate quantitative description of the spleen that has the potential to promote the diagnosis of spleen diseases. Moreover, such a segmentation model laid the root for further spleen classification and focal lesion segmentation.\u003c/p\u003e\u003cp\u003ePrevious studies have shown the age-related distribution of spleen volume [16\u0026ndash;18]. These morphological studies did not utilize the automated segmentation process based on deep learning. Presently, an increasing number of studies utilize spleen volume as a prognostic indicator for certain illness populations, but there is a deficiency of research applying 3D U-Net-based segmentation to segment the spleen in a large cohort of healthy individuals and to assess the resulting data. In this study, we established a 3D U-Net-based model for the automatic segmentation of spleens in abdominal CT images and tried to discover the changes in morphometrics of normal spleens in different age groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.1. patients\u003c/h2\u003e\u003cp\u003e This retrospective study was approved by the institutional review board of Peking University First Hospital [No. 2024 (222-004)], with a waiver of informed consent.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Training dataset for a 3D U-Net segmentation model\u003c/h2\u003e\u003cp\u003eA total of 2,856 image datasets were collected from the publicly available datasets Abdomen_CT_1k(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://abdomenct-1k-fully-supervised-learning.grand-challenge.org/),Flare23(https://codalab.lisn.upsaclay.fr/competitions/12239#learn_the_details-dataset\u003c/span\u003e\u003cspan address=\"https://abdomenct-1k-fully-supervised-learning.grand-challenge.org/),Flare23(https://codalab.lisn.upsaclay.fr/competitions/12239#learn_the_details-dataset\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), AMOS(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/record/7262581#.ZFNB6nZByUk\u003c/span\u003e\u003cspan address=\"https://zenodo.org/record/7262581#.ZFNB6nZByUk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and RSNA(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data\u003c/span\u003e\u003cspan address=\"https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which were subsequently utilized to train the model. The training dataset was randomly divided into training (n\u0026thinsp;=\u0026thinsp;2292), validation (n\u0026thinsp;=\u0026thinsp;280), and test (n\u0026thinsp;=\u0026thinsp;284) sets for the purpose of training the 3D U-Net segmentation model. This specific dataset wasn't used for quantitative analysis of the spleen.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Validation dataset for segmentation model and morphometric analysis of spleen\u003c/h2\u003e\u003cp\u003eWe collected patients who underwent abdominal CT examinations who were registered in our institution from January 2023 to December 2023, including both non-contrast computed tomography (NCCT) and contrast-enhanced computed tomography (CECT) examinations. The CT images were performed on four CT scanners in our hospital: Siemens SOMATOM Definition Flash CT (Siemens Healthcare), GE LightSpeed VCT (GE Healthcare), Philips Brilliance 256 iCT scan (Philips Healthcare), and GE Discovery CT750HD (GE Healthcare). The imaging parameters were as follows:slice thickness 1.25 mm and 1 mm. The patients were considered to have a \u0026ldquo;normal\u0026rdquo; spleen according to comprehensive clinical and imaging information. The inclusion criteria were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) images including the entire spleen; (3) medical imaging reports of a normal spleen; (4) no clinical history of spleen-related diseases, such as hematological disorders: hypersplenism, various anemias (aplastic, hemolytic, hereditary spherocytosis), leukemia (acute or chronic myeloid), lymphoma, macroglobulinemia, myelofibrosis; hepatic disorders: liver cirrhosis, portal hypertension, hepatitis, liver space-occupying lesions; neoplastic conditions: liver cancer, pancreatic cancer, pancreatic space-occupying lesions, or maintenance chemotherapy for malignancies; (5) there is no clinical history of an operation on the spleen. We also applied the following exclusion criteria: (1) Image quality defects include incomplete acquisition of examination images, insufficient scanning range that fails to cover the entire spleen, and respiratory motion artifacts that hinder accurate diagnosis; (2) anatomical abnormalities: postoperative absence of the spleen and significant deformity; (3) imaging abnormalities: re-evaluation of the scans indicates abnormal structure and density of the spleen, as well as suspected lesions existing in the spleen.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Model training\u003c/h2\u003e\u003cp\u003eThis study was based on a 3D U-Net architecture equipped with Nvidia Tesla P100 16G (Nvidia Corporation, Santa Clara, CA) GPU and PyTorch v1.7.1\u0026thinsp;+\u0026thinsp;cu110 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pytorch.org/\u003c/span\u003e\u003cspan address=\"https://pytorch.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The model inputs CT images and outputs spleen volume, average CT values, and three-dimensional diameters. Image preprocessing included window adjustment (center 30 HU, width 300 HU), resizing to 128 px \u0026times; 192 px \u0026times; 256 px, and image augmentation by rotating, sheering, noise injection, denoising, etc. The training parameters were batch size 6, learning rate 0.0001, and epoch 400.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Evaluation of the segmentation model\u003c/h2\u003e\u003cp\u003eObjective and subjective evaluations assessed the spleen segmentation model's performance. Dice similarity coefficient (DSC), volume similarity (VS), Hausdorff distance (HD), and average HD of model training and test sets were objective evaluation approaches. Two imaging radiologists also subjectively assessed the external verification data segmentation prediction structure. Inclusion of all spleen structures was evaluated. Whether elements outside the spleen (such as the splenic artery, vein, diaphragm, stomach wall, and intestinal tract) were included, the segmentation effect was unsatisfactory if the missing range of the spleen label surpassed 5% of its entire volume. The segmentation effect was insufficient if the spleen label extended outside the spleen and reached 5% of its volume. Images with poor segmentation were manually modified, and the final result was output.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Quantitative measurements\u003c/h2\u003e\u003cp\u003eThe image series in the external validation data underwent autolabeling by the spleen segmentation model. These spleen labels were checked by a junior radiologist and a senior radiologist together, and unsatisfied labels were modified manually. Necessary manual modification of labels was performed to ensure that the labelled area (as a region of interest [ROI]) contained the spleen only (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (a)-(d)), and adjacent structures, such as the gastric wall, kidney, or liver, were not covered. Labels that exceeded or missed 5% of spleen volume were also modified. The volume, average CT values, and three-dimensional diameters of the ROIs were measured.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e\u003cp\u003eThe statistical analysis was performed in R 4.4.2. All statistical significance values were set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The Shapiro-Wilk test determined data normality. If the Shapiro-Wilk test p-value was larger than 0.05, the t-test was employed for inter-group comparisons; otherwise, the two-sample Wilcoxon test was used. ANOVA was employed in multi-group comparisons if the data all corresponded to a normal distribution and passed the homogeneity of variance test; otherwise, the Kruskal-Wallis test was used.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. Spleen segmentation model evaluation\u003c/p\u003e\n\u003cp\u003eThe model\u0026apos;s quantitative results are presented in Table 1. In dataset 1, the testing dataset (N = 284) demonstrated segmentation performance with a Dice Similarity Coefficient (DSC) of 0.982 [0.975\u0026ndash;0.988], Volume Similarity (VS) of 0.995 [0.991, 0.998], Hausdorff Distance (HD) of 3.047 [2.578, 4.653] mm, and Average HD of 0.014 [0.009, 0.019] mm. In dataset 2, two radiologists assessed the segmentation predictions of the algorithm. Photos depicting focal or diffuse lesions in the spleen were removed, and photos with inadequate segmentation findings were manually adjusted. Ultimately, segmentation outcomes from 3,490 pictures were accessible for precise computation of spleen volume.\u003c/p\u003e\n\u003cp\u003e3.2. Morphometric analysis of\u0026nbsp;spleen\u003c/p\u003e\n\u003cp\u003eAs shown in Table 1, the average CT values of the male spleen in the non-contrast (NoC), arterial phase (AP), portal venous phase (PVP), and delayed phase (DP) were 43.86 [40.37, 46.93]. HU, 104.17 \u0026plusmn; 21.42 HU, 106.34 [97.71, 116.64] HU, 82.35 [75.18, 91.01] HU, respectively; average CT values of the female spleen in each enhancement phase were 43.43 [40.38, 46.8] HU, 120.72 [105.47, 135.33] HU, 114.82 [105.47, 125.11] HU, 86.73 [78.99, 96.22] HU, respectively. Analysis using the two-sample Wilcoxon test revealed that the average CT values of the female spleen throughout the arterial phase, portal venous phase, and delayed phase were all significantly greater than those of the male, with all differences being statistically significant (P \u0026lt; 0.001) (Fig. 3(a)).\u003c/p\u003e\n\u003cp\u003eTable 3 indicates that the volume distribution range of the male spleen (N = 506) was 213.74 [158.4, 284.88] cm\u0026sup3;, whereas the volume distribution range of the female spleen was 163.7 [125.99, 217.72] cm\u0026sup3;. In each age group, the spleen volume of male patients exceeded that of female patients. With the exception of the 18-27-year-old cohort, no statistically significant difference in spleen volume was observed between males and females (T-test, t = 1.38, P = 0.19). However, significant statistical differences in spleen volume between genders were noted in the remaining age groups (P values \u0026lt; 0.001). As aging progresses, the spleen volume in male patients exhibits an initial increase followed by a subsequent decrease, peaking between 28 and 37 years, with a maximum value of 273.75 \u0026plusmn; 92.96 cm\u0026sup3;. The variation in female spleen volume with age exhibited a bimodal distribution, attaining its maximum from 18 to 27 years, with a peak value of 225.01 \u0026plusmn; 65.53 cm\u0026sup3; (Fig. 3(b)). As age advances, the elevated CT value of the spleen exhibited a pattern of initial decline followed by subsequent increase (Figure 3(c)). The three-dimensional dimensions (x \u0026times; y \u0026times; z) of the spleen for all included patients were 9.2 [8.4, 9.95] cm \u0026times; 9.5 \u0026plusmn; 1.97 cm \u0026times; 9.4 \u0026plusmn; 1.91 cm for males and 8.45 \u0026plusmn; 1.09 cm \u0026times; 8.92 \u0026plusmn; 1.83 cm \u0026times; 8.64 \u0026plusmn; 1.67 cm for females, respectively (Figures 3(d)\u0026ndash;(f)).\u003c/p\u003e\n\u003cp\u003eTable 1 \u0026nbsp;Objective evaluation results of the spleen segmentation model\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eparameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eExternal validation set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eTesting set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eTraining set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003evalidation set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003en=3490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003en=284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003en=2292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003en=280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eDice similarity coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.736～1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.982[0.975,0.988]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.983[0.977,0.987]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.982[0.974,0.987]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5393.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eVolume similarity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.738～1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.995[0.991,0.998]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.996[0.992,0.998]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.996[0.991,0.998]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5313.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eHausdorff distance /mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0～57.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e3.047[2.578,4.653]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2.839[2.389,3.658]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3.094[2.528,4.713]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5263.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eAverage Hausdorff distance/mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0～21.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.014[0.009,0.019]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.013[0.009,0.017]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.014[0.01,0.021]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5317.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are described as \u0026nbsp;median [interquartile range] or minimum ~ maximum.\u003c/p\u003e\n\u003cp\u003eTable 2 \u0026nbsp;Quantitative parameters of spleen in different phase\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 243px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 232px;\"\u003e\n \u003cp\u003eNoc\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eN=1939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003eN=1551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eN=473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003eN=389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003eAverage CT value/HU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e88.52[61.56,105.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e96.91[59.04,116.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1297301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e43.86[40.37,46.93]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e43.43[40.38,46.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e96145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003eVolume/cm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e210.42[160.55,276.41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e161.68[125.3,216.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e2018213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e206.32[157.23,274.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e157.55[122.56,209.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e123943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003eDiameters/cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e9.18[8.40,9.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e8.54[7.82,9.22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1999402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e9.15\u0026plusmn;1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e8.48\u0026plusmn;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e8.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003ey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e9.37[8.07,10.73]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e8.82[7.58,10.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1777732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e9.31\u0026plusmn;1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e8.75\u0026plusmn;1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003e9.40[8.05,10.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e8.50[7.40,9.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e1895192.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e9.31\u0026plusmn;1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e8.40[7.40,9.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e119674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData conforming to a normal distribution are expressed as mean \u0026plusmn; standard deviation, otherwise as median [interquartile range].\u003c/p\u003e\n\u003cp\u003eTable 2 \u0026nbsp;Quantitative parameters of spleen in different phase(continued Table)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 219px;\"\u003e\n \u003cp\u003eAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 159px;\"\u003e\n \u003cp\u003ePVP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003eDP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003eN=427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003eN=317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003eN=506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003eN=441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003eN=533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49px;\"\u003e\n \u003cp\u003eN=404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e104.17\u0026plusmn;21.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e120.72[105.47,135.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e39127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e106.34[97.71,116.64]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e114.82[105.47,125.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e76560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e82.35[75.18,91.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e86.73[78.99,96.22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e84697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e204.18[157.5,270.18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e158.87[124.47,212.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e91044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e213.74[158.4,284.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e163.7[125.99,217.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e149411.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e215.14[169.59,278.78]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e167.94[127.98,226.22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e143889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e9.08\u0026plusmn;1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e8.59[7.8,9.23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e86483.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e9.2[8.4,9.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e8.45\u0026plusmn;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e151433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e9.27\u0026plusmn;1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e8.65[7.93,9.28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e143818.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e9.34[8.21,10.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e8.78\u0026plusmn;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e82694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e9.5\u0026plusmn;1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e8.92\u0026plusmn;1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e9.51\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e8.86\u0026plusmn;1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e9.44\u0026plusmn;1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e8.50[7.50,9.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e85111.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e9.40\u0026plusmn;1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e8.64\u0026plusmn;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e6.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e9.50[8.10,10.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e8.57[7.60,9.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e134317.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Quantitative parameters of spleen in different age groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"103%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e18-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e28-37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eN=506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eN=441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eN=9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003eN=20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eN=16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eN=34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003eAverage CT value/HU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e106.34[97.71,116.64]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e114.82[105.47,125.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e76560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e118.16\u0026plusmn;12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e122.08\u0026plusmn;17.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e103.38\u0026plusmn;10.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e110.87[105.07,124.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003eVolume/cm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e213.74[158.40,284.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e163.70[125.99,217.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e149411.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e267.56\u0026plusmn;81.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e225.01\u0026plusmn;65.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e273.75\u0026plusmn;92.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e183.33\u0026plusmn;63.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003eDiameters/cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e9.20[8.04,9.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e8.45\u0026plusmn;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e151433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e9.12\u0026plusmn;1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e8.76\u0026plusmn;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e9.52\u0026plusmn;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e8.55\u0026plusmn;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003ey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e9.5\u0026plusmn;1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e8.92\u0026plusmn;1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e10.71\u0026plusmn;1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e10.16\u0026plusmn;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e10.89\u0026plusmn;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e9.3\u0026plusmn;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e9.40\u0026plusmn;1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e8.64\u0026plusmn;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e6.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e10.28\u0026plusmn;1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e9.53\u0026plusmn;1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e10.46\u0026plusmn;1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e8.94\u0026plusmn;1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData conforming to a normal distribution are expressed as mean \u0026plusmn; standard deviation, otherwise as median [interquartile range].\u003c/p\u003e\n\u003cp\u003eTable 3 Quantitative parameters of spleen in different age groups(continued Table)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 137px;\"\u003e\n \u003cp\u003e38-47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e48-57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e58-67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003eN=27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003eN=38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eN=74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003eN=77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eN=165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003eN=116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e106.22\u0026plusmn;14.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e113.12\u0026plusmn;16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e-1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e106.05[97.68,114.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e113.35\u0026plusmn;14.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e103.96[95.88,112.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e114.51[105.79,124.28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e5568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e248.97\u0026plusmn;62.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e184.15[140.2,233.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e247[192.50,322.56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e198.48[153.55,252.44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e217.04[159.9,288.69]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e162.5[127.18,218.46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e12938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e9.44\u0026plusmn;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e8.49\u0026plusmn;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e9.07[8.34,10.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e8.72\u0026plusmn;1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e9.13\u0026plusmn;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e8.44\u0026plusmn;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e9.87\u0026plusmn;1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e9.60\u0026plusmn;1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e10.04\u0026plusmn;1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e9.23[8.36,10.56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e9.63\u0026plusmn;2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e8.78\u0026plusmn;1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e10.30\u0026plusmn;1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e8.95\u0026plusmn;1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e10.39\u0026plusmn;1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e9.43\u0026plusmn;1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e9.45\u0026plusmn;1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e8.70\u0026plusmn;1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 Quantitative parameters of spleen in different age groups(continued Table)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"482\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e68-77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 185px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u003c/strong\u003e\u003cstrong\u003e78\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eStatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eN=156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eN=107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003eN=59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003eN=49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e107.97\u0026plusmn;14.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e115.48\u0026plusmn;12.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e117.41\u0026plusmn;21.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e123.29\u0026plusmn;19.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e188.18[141.21,255.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e141.23[102.34,170.86]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e11992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e171.27[129.04,228.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e143.15\u0026plusmn;50.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e1944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e9.18\u0026plusmn;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e8.27\u0026plusmn;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e8.98\u0026plusmn;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8.2\u0026plusmn;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e9.07\u0026plusmn;1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e8.28\u0026plusmn;1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e8.57[7.56,9.62]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8.31\u0026plusmn;1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e1585.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e8.95\u0026plusmn;1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e8.04\u0026plusmn;1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e8.42\u0026plusmn;1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e7.76\u0026plusmn;1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eQuantitative imaging refers to the extraction of measurable characteristics from medical pictures. These features are utilized to evaluate the severity, extent of alteration, or condition status in comparison to normalcy versus sickness, damage, or chronic conditions [19]. Compared to traditional visual assessment, quantitative imaging offers a more objective and accurate dataset for evaluation while enabling dynamic analysis that aids in monitoring subtle changes during longitudinal follow-up (e.g., the yearly growth rate of spleen volume). Recent years have shown substantial progress in medical image segmentation approaches utilizing the U-Net architecture. Multiple studies have documented the practicality and therapeutic use of applying 3D U-Net for organ segmentation [20\u0026ndash;22]. The 3D U-Net model employed in this study enables accurate segmentation of the spleen in CT images and rapid acquisition of quantitative metrics, including volume (cm\u0026sup3;), average CT value (HU), and three-dimensional dimensions (cm). We expect these measurement results to enhance clinical decision-making, specifically in portal hypertension grading and cancer staging. The model exhibited consistent performance throughout the training set (n\u0026thinsp;=\u0026thinsp;2292), tuning set (n\u0026thinsp;=\u0026thinsp;280), and test set (n\u0026thinsp;=\u0026thinsp;284), signifying robust generalization capabilities. Given the model's outstanding segmentation performance on the training dataset, its applicability in more intricate clinical circumstances is a reasonable suggestion. This study developed a spleen segmentation model for thin-slice CT images utilizing the 3D U-Net architecture, capable of performing automatic segmentation of spleens with a substantial sample size. This study selected thin-slice images from the portal venous phase (PVP) for statistical analysis of spleen volume, given the relatively high organ display contrast, extensive scanning range, and routine thin-slice reconstruction in this phase. The cohort exhibiting adequate segmentation effects underwent an analysis of the relationship between spleen volume and age. The results indicated that male spleen volume exhibited an initial increase followed by a fall, whereas female spleen volume initially reduced, then climbed, and subsequently declined after reaching a high. Prior research indicated a trend of decreasing spleen volume with advancing age [16\u0026ndash;18, 8]. In contrast, this study revealed that the male spleen volume exhibited an inverted U-shaped trajectory with age, whereas the female spleen volume demonstrated a bimodal distribution with age. The findings may indicate that hormonal regulation governs spleen volume. The hormonal variations preceding and following menopause in women may elucidate the sub-peak phenomena, whereas the comparatively elevated basal metabolic rate in young and middle-aged men may facilitate the compensatory hypertrophy of the spleen [23].\u003c/p\u003e\u003cp\u003eHistorically, several study publications have addressed spleen volume, predominantly utilizing data from other nations. Currently, no research has been undertaken on the normative range of spleen volume in the Chinese patient population. Geraghty and colleagues [16] discovered that the mean spleen capacity was 209 cm\u0026sup3; (N\u0026thinsp;=\u0026thinsp;420), predominantly sourced from the North American population. Ardene Harris and colleagues [17] reported that the mean spleen volume in the Japanese population was 127.4 cm\u0026sup3;, with a standard deviation of 62.9 cm\u0026sup3; (N\u0026thinsp;=\u0026thinsp;230). A separate study [18] determined that the mean spleen volume in the Japanese population was 123 cm\u0026sup3;, with a standard deviation of 45 cm\u0026sup3; (N\u0026thinsp;=\u0026thinsp;238). The researchers established the spleen volume ranges provided above based on data collected from each of their respective locations. The spleen volumes across different groups, as shown in several studies, exhibit significant variation, maybe attributable to ethnic variances. This variation is detectable. This study established that the spleen capacity of adult Chinese males was 213.74 [158.4, 284.88] cm\u0026sup3;, whereas that of females was 163.7 [125.99, 217.72] cm\u0026sup3;. This study, the inaugural large-sample investigation (N\u0026thinsp;=\u0026thinsp;1520) of the Chinese population, establishes reference values for spleen volume within this demographic.\u003c/p\u003e\u003cp\u003eThis study possesses multiple limitations that require elucidation. Initially, while patients with conditions that distinctly influence spleen volume have been omitted from the validation dataset, the remaining population may possess potential confounding variables, including undetected subclinical infections and metabolic irregularities, which could distort the measurements of spleen volume. Secondly, the study failed to thoroughly examine the impact of anthropometric characteristics, including height and weight, on spleen volume. Nevertheless, current research indicates that these elements are intricately associated with spleen development. In future determinations of standard reference intervals, variables such as body surface area and body mass index (BMI) must be incorporated for multifactorial analysis. Furthermore, the existing model is predicated on individuals exhibiting normal spleen morphology, and its applicability to anatomical deviations (such as the spleen-liver wrapping phenomena in patients with beaver tail liver) and clinical conditions is constrained. It is essential to enhance the algorithm's robustness by incorporating intricate scenarios. Single-center studies are susceptible to selection bias at the data level, and the uneven age distribution may influence the generalizability of the findings. Further multicenter and multiethnic large-sample studies must be conducted to enhance generalizability.It is important to acknowledge that while the observed gender disparity in spleen volume is statistically significant, its biological explanation requires more investigation in conjunction with hormone levels, body composition studies, and other factors.The identified limitations indicate that the clinical application of this study's conclusions should be approached with caution; however, they also highlight avenues for optimization in future research, such as enhancing sample diversity, incorporating multimodal data, and reinforcing algorithmic validation in complex scenarios.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, the spleen segmentation model developed using 3D U-Net has demonstrated commendable segmentation efficacy. The ongoing advancement of technology and the incorporation of automated technologies into the workplace are anticipated to facilitate the precise volumetric evaluation of the spleen in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eDeclaration of No Funding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eI hereby declare that this study's research has not received any specific financial support from the public, commercial or non-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo forms of financial aid, such as scientific research funds from government agencies, corporate sponsorships or grants from charitable organisations, have been obtained during the conduct of this study. All the resources required for the research, including experimental materials, equipment usage and personnel input, have been obtained through non-financial support channels, such as the institution's routine resource allocation, personal efforts and academic cooperation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eI am responsible for the accuracy of the above declaration. Should any of the above information prove to be false, I am willing to accept the corresponding academic and legal consequences.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions Statement:T.T.: Data collection, image evaluation, article writing, statistical analysis and visualisation.- Z.X., Y.Z., X.Z.: Model training and data management.- X.W.: Project planning, management and guidance; article revision.All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eG. M. Crane, Y.-C. Liu, and A. 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(Warsz.)\u003c/em\u003e, vol. 65, no. 2, pp. 123–136, Apr. 2017, doi: 10.1007/s00005-016-0418-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Spleen, morphometric, deep learning, computed tomography","lastPublishedDoi":"10.21203/rs.3.rs-7584704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7584704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo perform quantitative measurements of the spleen's morphological properties and to develop a 3D U-Net-based segmentation and automatic model measurement of the normal spleen using CT images. Additionally, the study intends to examine age-related changes in the normal spleen's volume as well as the enhancement characteristics of the normal spleen on contrast-enhanced CT scans.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 2856 images of spleens(dataset 1) were collected from four public sources and randomly split into three groups: training (2292 images), validation (280 images), and test (284 images). The segmentation efficiency for the spleen was evaluated by the Dice similarity coefficient (DSC), volume similarity (VS), Hausdorff distance (HD), and average HD. Another dataset of 3490 normal spleen CT images (dataset 2) was obtained for external validation, including 862 non-contrast images, 744 arterial phase images, 947 portal venous phase images, and 937 delayed phase images. Then, 947 portal venous phase image series of inpatient contrast-enhanced abdominal CT examinations with normal spleen were included for analysis and grouped by every 10-year gap. A 3D U-Net-based segmentation model was used to predict spleen labels, followed by manual label modifications as appropriate. Quantitative parameters of the spleen (volume, CT value, and diameter) were then analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn dataset 1, the testing dataset (N = 284) showed segmentation performance with a Dice Similarity Coefficient (DSC) of 0.982 [0.975–0.988], Volume Similarity (VS) of 0.995 [0.991,0.998] , Hausdorff Distance (HD) of 3.047 [2.578, 4.653] mm, and Average HD of 0.014 [0.009, 0.019] mm. In dataset 2, the distribution ranges of the three-dimensional diameters (x, y, z) of the spleen were as follows: for males, 9.20 [8.40 - 9.95] cm (median [interquartile range]), 9.50 ± 1.97 cm (mean ± standard deviation), and 9.40 ± 1.91 cm; for females, 9.50 ± 1.09 cm, 8.92 ± 1.83 cm, and 8.64 ± 1.67 cm. The distribution range of the spleen volume was 213.74 [158.4, 284.88] cm³ for males and 163.7 [125.99, 217.72] cm³ for females. In the enhanced scan images of the spleen, it was found that the CT values of the spleen in the arterial phase, portal venous phase, and delayed phase were all higher in females than in males. With the increase in age, the spleen volume of males showed a trend of first increasing and then decreasing, reaching its peak at the age of 28–37, with a peak value of 273.75 ± 92.96 cm³. The spleen volume of females gradually decreased with the increase of age, with the peak value of 225.01 ± 65.53 cm³. In all age groups, the spleen volume of females was smaller than that of males.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spleen segmentation tool based on deep learning can segment the spleen on CT images and measure its normal diameter, volume, and CT value accurately and effectively.\u003c/p\u003e","manuscriptTitle":"Deep - Learning - Based Automatic Segmentation and Quantitative Measurement of Normal Spleen in Chinese Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 02:13:55","doi":"10.21203/rs.3.rs-7584704/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4b642502-16a7-4b72-bf81-9bac10cc0362","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-05T11:08:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-28 02:13:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7584704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7584704","identity":"rs-7584704","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}