{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T08:41:59Z","timestamp":1775983319700,"version":"3.50.1"},"reference-count":35,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T00:00:00Z","timestamp":1761868800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T00:00:00Z","timestamp":1761868800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Recent advancements in deep learning have greatly impacted medical image segmentation, especially in the segmentation of human organs and tissues. Accurate segmentation is crucial for precise diagnoses and effective treatment planning. However, segmenting anal fistulae in magnetic resonance imaging (MRI) images presents a significant challenge due to the similarity between lesions and normal tissues, which often results in high false positive\u00a0rates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to develop a robust segmentation method for anal fistulae in MRI images that improves accuracy by reducing false positives, while also efficiently handling the complexity of multi\u2010modal and multi\u2010directional MRI\u00a0data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We propose a novel dual\u2010module framework. At the input stage, a inspired method is employed to efficiently capture multi\u2010dimensional information from the MRI data. At the output stage, residual techniques are applied to optimize and enhance the segmentation results, ensuring comprehensive integration of multi\u2010modal and multi\u2010directional MRI information. Additionally, an Outlier\u2010Penalized Dice Loss (OPDL) function is introduced to specifically address the issue of false positives in\u00a0segmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed method was evaluated on a dataset of 950 self\u2010collected multi\u2010directional MRI scans. Our approach demonstrated superior segmentation performance compared to six state\u2010of\u2010the\u2010art methods, achieving a Dice Score of 0.7324 and an IoU Score of 0.5943, which represents significant improvements over the highest\u2010performing baseline\u00a0models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The dual\u2010module framework, combined with the selection mechanism and residual techniques, along with the innovative OPDL, offers a significant advancement in the segmentation of anal fistulae in MRI images. This approach not only improves accuracy by effectively distinguishing between lesions and normal tissues but also reduces computational complexity, making it a promising tool for clinical\u00a0applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70102","type":"journal-article","created":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T03:40:38Z","timestamp":1761968438000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A dual\u2010module 3D fusion framework for multi\u2010modal MRI segmentation in anal fistulae"],"prefix":"10.1002","volume":"52","author":[{"given":"Genlang","family":"Chen","sequence":"first","affiliation":[{"name":"NingboTech University Ningbo China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang University Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajian","family":"Zhang","sequence":"additional","affiliation":[{"name":"NingboTech University Ningbo China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Cai","sequence":"additional","affiliation":[{"name":"Ningbo No.2 Hospital Ningbo China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianyun","family":"Meng","sequence":"additional","affiliation":[{"name":"Ningbo No.2 Hospital Ningbo China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,10,31]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-017-9983-4"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-019-00227-x"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/0730-725X(94)00124-L"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/9066128"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s43055-023-00989-z"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-54441-2"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.23642"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/450341"},{"key":"e_1_2_8_10_1","first-page":"65","article-title":"Magnetic resonance imaging (MRI)\u2010a review","volume":"3","author":"Katti G","year":"2011","journal-title":"Int J Dent Clinics"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.03.006"},{"key":"e_1_2_8_12_1","unstructured":"LindeijerTN YtredalTM Eftest\u00f8lT et\u00a0al.Leveraging Multi\u2010View Data without Annotations for Prostate MRI Segmentation: A Contrastive Approach. arXiv preprint arXiv:2308.06477 2023."},{"key":"e_1_2_8_13_1","doi-asserted-by":"crossref","unstructured":"MortaziA KarimR RhodeK BurtJ BagciU.CardiacNET: Segmentation of left atrium and proximal pulmonary veins from MRI using multi\u2010view CNN. 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