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This limitation leads to suboptimal performance in the boundary segmentation of complex anatomical structures. (2) Inefficiency in long\u2010range modeling: While Transformer\u2010based methods can capture global dependencies, they suffer from quadratic growth in computational complexity and the risk of overfitting when applied to high\u2010resolution data (e.g., 3D medical images), making it difficult to balance efficiency and\u00a0accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To address the above challenges, this article proposes a Tri\u2010branch Collaborative Consistency model based on Mamba long\u2010range modeling (TCC\u2010Mamba), which aims to reduce reliance on annotations while improving segmentation accuracy in complex regions of medical\u00a0images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>TCC\u2010Mamba consists of a shared encoder and a tri\u2010branch decoder. Specifically, a tri\u2010branch collaborative supervision mechanism is introduced where three decoders form a closed\u2010loop learning system through cross\u2010pseudo\u2010label supervision, enabling collaborative optimization and information sharing. Additionally, a geometric consistency loss function is incorporated to enhance boundary awareness. Furthermore, we integrate the SpatialTriMamba module, leveraging the efficient long\u2010range dependency modeling of state\u2010space models to achieve dynamic fusion of global context and local features, thereby improving segmentation accuracy for complex\u00a0boundaries.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We conducted experiments on three public datasets: Left Atrium (LA), Pancreas CT, and ACDC, using 10%, 20%, and 30% labeled data. The results demonstrate that our method outperforms the six current advanced semi\u2010supervised methods, achieving better segmentation performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The TCC\u2010Mamba introduces novel methodologies in medical image segmentation tasks. This model combines the SpatialTriMamba module to capture long\u2010range features and utilizes signed distance maps to enhance the use of geometric information, leading to exceptional results in handling complex anatomical structures. It provides an efficient and reliable solution for semi\u2010supervised medical image\u00a0segmentation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70390","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T05:25:42Z","timestamp":1778736342000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Triple collaborative consistency with Mamba for semi\u2010supervised 3D medical image segmentation"],"prefix":"10.1002","volume":"53","author":[{"given":"Yufei","family":"Gao","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering Zhengzhou University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingning","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering Zhengzhou University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Beijing Institute of Technology  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yameng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Pathology Henan Provincial People's Hospital; People's Hospital of Zhengzhou University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujie","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Pathology Henan Provincial People's Hospital; People's Hospital of Zhengzhou University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-1009-y"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","unstructured":"TangH ZhangC XieX.Automatic pulmonary lobe segmentation using deep learning. 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