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The limitations of supervised deep learning approaches in terms of annotation cost and applicability necessitate the exploration of alternative approaches that can effectively address these challenges and facilitate the real\u2010world clinical deployment of automatic 3D cerebrovascular segmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To address the challenges of limited labeled data by exploiting the intricate structures of vessels and developing a method to assess the reliability of generated pseudo\u2010labels, with the ultimate goal of enhancing the efficiency of unlabeled data utilization and improving segmentation accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We introduce a cross\u2010consistency dual uncertainty quantification mean teacher method for semi\u2010supervised learning fine\u2010grained 3D cerebrovascular segmentation from TOF\u2010MRA images. To effectively incorporate knowledge from unlabeled samples, we present a dual\u2010consistency learning approach that jointly pertains to pixel\u2010image transformation consistent equivariant and feature perturbation invariance. Following that, in an attempt to guarantee more confidence in unsupervised learning, we evaluate the segmentation uncertainty using the predictions from both the student and teacher models and employ them in collaboration for guiding consistency regularization. Additionally, we boost the pixel\u2010level prediction performance by employing a region\u2010specific supervised loss only for the annotated input samples.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Quantitative and qualitative results on two publicly available datasets show that the proposed method yielded better results than state\u2010of\u2010the\u2010art semi\u2010supervised learning methods for cerebrovascular segmentation. Specifically, our method achieved a dice similarity coefficient of 83.3% and intersection\u2010over\u2010union of 71.5% on the IXI dataset, surpassing the baseline uncertainty\u2010aware mean teacher method by 1.7% and 2.8%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The framework's ability to achieve competitive performance across various metrics showcases its potential for relieving human annotation efforts for accurate cerebrovascular extraction tasks, where its effectiveness in handling unlabeled data can offer significant advantages.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70017","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T12:55:49Z","timestamp":1758632149000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing semi\u2010supervised learning for fine\u2010grained 3D cerebrovascular segmentation with cross\u2010consistency and uncertainty estimation"],"prefix":"10.1002","volume":"52","author":[{"given":"Yousuf Babiker M.","family":"Osman","sequence":"first","affiliation":[{"name":"Paul C. 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Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences  Shenzhen China"},{"name":"University of Chinese Academy of Sciences Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Research Center for Biomedical Information Technology, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Wang","sequence":"additional","affiliation":[{"name":"Paul C. 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