DCSSM: Enhancing Medical Organ Segmentation with Positional Correlation and Feature Alignment

Medical organ segmentation is essential for accurately identifying the underlying causes of diseases and developing appropriate treatment plans. Current distribution methods primarily focus on the segmentation task, often neglecting the relationship between image distribution and segmentation. Effective image distribution aligns features and can also serve as a boundary constraint in the segmentation task. The high cost of annotating medical images limits the number of available labeled samples. Furthermore, effective methods for extracting organ positional information between image slices are lacking. This paper introduces a distribution-constrained semi-supervised segmentation method (DCSSM). We first design a Category Center of Mass Calculation Module to capture the positional correlation between image slices. Next, we use a public encoder and distribution network for pretraining, enabling segmentation of a large number of unlabeled samples with only a few labeled samples. We also design a lightweight multiscale feature extraction module and use it to build the segmentation network, achieving rapid feature extraction without compromising performance. Finally, the pretrained public encoder and distribution network align the features of the input images, with the aligned boundaries acting as constraints to guide mask generation in the segmentation network. The proposed method is evaluated through comparative and ablation experiments on the publicly available Synapse and skin lesion datasets, against eight baseline methods. We further validate the generalization ability of the proposed method by applying it to skin lesion segmentation datasets. Experimental results show that the proposed method achieves optimal performance across multiple evaluation metrics.