{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:09:51Z","timestamp":1776888591020,"version":"3.51.2"},"reference-count":32,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T00:00:00Z","timestamp":1719964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC2704100"],"award-info":[{"award-number":["2022YFC2704100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024-P-7"],"award-info":[{"award-number":["2024-P-7"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of Aizu","award":["2022YFC2704100"],"award-info":[{"award-number":["2022YFC2704100"]}]},{"name":"University of Aizu","award":["2024-P-7"],"award-info":[{"award-number":["2024-P-7"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Automated perimetrium segmentation of transvaginal ultrasound images is an important process for computer-aided diagnosis of uterine diseases. However, ultrasound images often contain various structures and textures, and these structures have different shapes, sizes, and contrasts; therefore, accurately segmenting the parametrium region of the uterus in transvaginal uterine ultrasound images is a challenge. Recently, many fully supervised deep learning-based methods have been proposed for the segmentation of transvaginal ultrasound images. Nevertheless, these methods require extensive pixel-level annotation by experienced sonographers. This procedure is expensive and time-consuming. In this paper, we present a bidirectional copy\u2013paste Mamba (BCP-Mamba) semi-supervised model for segmenting the parametrium. The proposed model is based on a bidirectional copy\u2013paste method and incorporates a U-shaped structure model with a visual state space (VSS) module instead of the traditional sampling method. A dataset comprising 1940 transvaginal ultrasound images from Tongji Hospital, Huazhong University of Science and Technology is utilized for training and evaluation. The proposed BCP-Mamba model undergoes comparative analysis with two widely recognized semi-supervised models, BCP-Net and U-Net, across various evaluation metrics including Dice, Jaccard, average surface distance (ASD), and Hausdorff_95. The results indicate the superior performance of the BCP-Mamba semi-supervised model, achieving a Dice coefficient of 86.55%, surpassing both U-Net (80.72%) and BCP-Net (84.63%) models. The Hausdorff_95 of the proposed method is 14.56. In comparison, the counterparts of U-Net and BCP-Net are 23.10 and 21.34, respectively. The experimental findings affirm the efficacy of the proposed semi-supervised learning approach in segmenting transvaginal uterine ultrasound images. The implementation of this model may alleviate the expert workload and facilitate more precise prediction and diagnosis of uterine-related conditions.<\/jats:p>","DOI":"10.3390\/diagnostics14131423","type":"journal-article","created":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T08:45:34Z","timestamp":1719996334000},"page":"1423","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Bidirectional Copy\u2013Paste Mamba for Enhanced Semi-Supervised Segmentation of Transvaginal Uterine Ultrasound Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9226-6406","authenticated-orcid":false,"given":"Boyuan","family":"Peng","sequence":"first","affiliation":[{"name":"Graduate Department of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Graduate Department of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenwen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4376-0806","authenticated-orcid":false,"given":"Xin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Graduate Department of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat. 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