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However, it remains a challenging task due to low soft\u2010tissue contrast and structural ambiguity in the gland's appearance. While recent deep learning methods have focused on refining network structures, they often neglect the inherent anatomical consistency of the\u00a0prostate.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To address this limitation, a novel segmentation framework integrating an Anatomy\u2010Aware Fusion Attention (AAFA) module is\u00a0proposed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>By leveraging anatomical templates, our approach establishes a multilevel feature cross\u2010attention mechanism that enhances global contextual modeling of prostate regions. Additionally, we introduce a Phased Learning strategy that progressively trains the model to mitigate the adverse effects of invalid or noisy samples commonly found in clinical MRI\u00a0data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Extensive ablation studies on the PROMISE12 dataset validate the contribution of each component to overall performance. Comparison experiments on both PROMISE12 and MSD Prostate datasets show that our method consistently outperforms existing approaches across key metrics, such as Dice similarity coefficient (DSC), Intersection over Union (IoU), Precision and 95% Hausdorff distance (HD95).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These results confirm the robustness and strong generalization capability of the proposed framework in challenging clinical segmentation\u00a0tasks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70669","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T06:57:12Z","timestamp":1788505032000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Anatomy\u2010aware fusion attention network for high\u2010precision prostate MRI segmentation"],"prefix":"10.1002","volume":"53","author":[{"given":"Yulong","family":"Wang","sequence":"first","affiliation":[{"name":"College of Information, Mechanical and Electrical Engineering, Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Information, Mechanical and Electrical Engineering, Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Information, Mechanical and Electrical Engineering, Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongqing","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering East China University of Science and Technology Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunlin","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Southeast University Nanjing Jiangsu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feiniu","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Information, Mechanical and Electrical Engineering, Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingcang","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Radiology Shanghai Pudong New Area Gongli Hospital Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","unstructured":"LongJ ShelhamerE DarrellT.Fully convolutional networks for semantic segmentation. 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