O-CCR: Oriented Cervical Canal Region Detection Framework for Biomechanical Cervical Assessment in TVUS

O-CCR: Oriented Cervical Canal Region Detection Framework for Biomechanical Cervical Assessment in TVUS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article O-CCR: Oriented Cervical Canal Region Detection Framework for Biomechanical Cervical Assessment in TVUS Minseo Hwangbo, Yeong-Eun Jeon, Kyong-No Lee, Keun-Young Lee, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6833446/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Dec, 2025 Read the published version in BMC Medical Imaging → Version 1 posted 10 You are reading this latest preprint version Abstract Background: The cervix undergoes biomechanical changes during pregnancy in preparation for delivery. Assessing the progression of these changes using transvaginal ultrasound (TVUS) is crucial for preterm birth prediction. However, existing methods such as cervical length have limitations in capturing subtle tissue changes. Although tissue analysis using TVUS has been explored to address these limitations, achieving consistent and reproducible results in quantitative analysis remains challenging due to high inter-observer variability and a lack of standardized region of interest (ROI) definitions. This study proposes an oriented cervical canal region (O-CCR) framework that identifies a standardized CCR with relevant sonographic features for evaluating biomechanical change. Methods: We utilized 1436 TVUS images for training, validation, and testing, with 189 additional images from a different hospital for external validation. CCR was defined to include the cervical canal and its surrounding region after aligning the IO and EO parallel to ensure anatomical consistency in the cervix. To validate the effectiveness of O-CCR in handling various orientations, we applied five oriented object detection models (Oriented R-CNN, ReDet, S \(^{2}\) A-Net, R \(^{3}\) Det, and Oriented RepPoints) and evaluated their CCR localization performance. Results: We compared the performance of five models implemented within O-CCR framework. Among them, Oriented RepPoints achieved the highest average precision (AP) of 0.981 at the intersection over union (IoU) threshold of 0.5, compared to Oriented R-CNN (0.968), S \(^{2}\) A-Net (0.962), ReDet (0.964), and R \(^{3}\) Det (0.980) on the test dataset. Notably, Oriented RepPoints demonstrated superior performance even at higher thresholds of 0.6 (0.931) and 0.7 (0.743) and the lowest average orientation error (AOE) of 9.1980 in CCR localization. Conclusions: O-CCR showed reliable performance to localize CCR despite various orientations and shapes, providing standardized regions for assessing biomechanical changes of the cervix. The consistent CCR could be applied to quantitative analysis of cervical tissue properties in future research. Ultimately, this approach could support the development of automated cervical change assessment for prenatal care. Deep learning Oriented Object Detection Cervix Cervical Canal Region (CCR) Transvaginal Ultrasound (TVUS) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Dec, 2025 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Revision requested 14 Aug, 2025 Reviews received at journal 06 Aug, 2025 Reviews received at journal 29 Jul, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers agreed at journal 18 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 08 Jul, 2025 Editor invited by journal 18 Jun, 2025 Submission checks completed at journal 18 Jun, 2025 First submitted to journal 18 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6833446","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485322555,"identity":"51821093-9865-42e9-bdff-dff786a5ea53","order_by":0,"name":"Minseo Hwangbo","email":"","orcid":"","institution":"Hallym University","correspondingAuthor":false,"prefix":"","firstName":"Minseo","middleName":"","lastName":"Hwangbo","suffix":""},{"id":485322556,"identity":"a06f33dc-c871-4194-895a-3a49048f0311","order_by":1,"name":"Yeong-Eun Jeon","email":"","orcid":"","institution":"Hallym 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Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep learning, Oriented Object Detection, Cervix, Cervical Canal Region (CCR), Transvaginal Ultrasound (TVUS)","lastPublishedDoi":"10.21203/rs.3.rs-6833446/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6833446/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe cervix undergoes biomechanical changes during pregnancy in preparation for delivery. Assessing the progression of these changes using transvaginal ultrasound (TVUS) is crucial for preterm birth prediction. However, existing methods such as cervical length have limitations in capturing subtle tissue changes. Although tissue analysis using TVUS has been explored to address these limitations, achieving consistent and reproducible results in quantitative analysis remains challenging due to high inter-observer variability and a lack of standardized region of interest (ROI) definitions. This study proposes an oriented cervical canal region (O-CCR) framework that identifies a standardized CCR with relevant sonographic features for evaluating biomechanical change.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe utilized 1436 TVUS images for training, validation, and testing, with 189 additional images from a different hospital for external validation. CCR was defined to include the cervical canal and its surrounding region after aligning the IO and EO parallel to ensure anatomical consistency in the cervix. To validate the effectiveness of O-CCR in handling various orientations, we applied five oriented object detection models (Oriented R-CNN, ReDet, S\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^{2}\\)\u003c/span\u003e\u003c/span\u003eA-Net, R\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^{3}\\)\u003c/span\u003e\u003c/span\u003eDet, and Oriented RepPoints) and evaluated their CCR localization performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe compared the performance of five models implemented within O-CCR framework. Among them, Oriented RepPoints achieved the highest average precision (AP) of 0.981 at the intersection over union (IoU) threshold of 0.5, compared to Oriented R-CNN (0.968), S\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^{2}\\)\u003c/span\u003e\u003c/span\u003eA-Net (0.962), ReDet (0.964), and R\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^{3}\\)\u003c/span\u003e\u003c/span\u003eDet (0.980) on the test dataset. Notably, Oriented RepPoints demonstrated superior performance even at higher thresholds of 0.6 (0.931) and 0.7 (0.743) and the lowest average orientation error (AOE) of 9.1980 in CCR localization.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eO-CCR showed reliable performance to localize CCR despite various orientations and shapes, providing standardized regions for assessing biomechanical changes of the cervix. The consistent CCR could be applied to quantitative analysis of cervical tissue properties in future research. Ultimately, this approach could support the development of automated cervical change assessment for prenatal care.\u003c/p\u003e","manuscriptTitle":"O-CCR: Oriented Cervical Canal Region Detection Framework for Biomechanical Cervical Assessment in TVUS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 12:10:06","doi":"10.21203/rs.3.rs-6833446/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-14T16:04:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-06T06:18:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T18:09:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260334897282578410489249318855953540210","date":"2025-07-25T04:03:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259272953217495949155601189004646976094","date":"2025-07-18T17:31:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T17:09:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-08T11:05:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-18T06:10:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-18T04:50:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-06-18T04:46:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ce9f8fe-0eea-40d6-a026-43b385dbad42","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:00:03+00:00","versionOfRecord":{"articleIdentity":"rs-6833446","link":"https://doi.org/10.1186/s12880-025-02056-7","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2025-12-16 15:57:22","publishedOnDateReadable":"December 16th, 2025"},"versionCreatedAt":"2025-07-15 12:10:06","video":"","vorDoi":"10.1186/s12880-025-02056-7","vorDoiUrl":"https://doi.org/10.1186/s12880-025-02056-7","workflowStages":[]},"version":"v1","identity":"rs-6833446","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6833446","identity":"rs-6833446","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}