{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T19:55:33Z","timestamp":1777924533415,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100013254","name":"the National College Students Innovation and Entrepreneurship Training Program","doi-asserted-by":"crossref","award":["202413980003Z"],"award-info":[{"award-number":["202413980003Z"]}],"id":[{"id":"10.13039\/501100013254","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Clinical Major Project of Changzhou Medical Center of Nanjing Medical University","award":["CMCM202305"],"award-info":[{"award-number":["CMCM202305"]}]},{"name":"the Science and Technology Development Fund of Nanjing Medical University","award":["NMUB20230275"],"award-info":[{"award-number":["NMUB20230275"]}]},{"name":"the Post-Doctor Research Funding of Changzhou Medical Center","award":["CZKYCMCP202313"],"award-info":[{"award-number":["CZKYCMCP202313"]}]},{"name":"the Talent Project of Kangda College of Nanjing Medical University","award":["KD2024JXJH007"],"award-info":[{"award-number":["KD2024JXJH007"]}]},{"name":"the Key R&D of Lianyungang City (Social Development) Project","award":["SF2318"],"award-info":[{"award-number":["SF2318"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate identification and localization of prostate zones in magnetic resonance (MR) images are essential for clinical diagnosis and treatment planning. However, convolutional object detection models like YOLO often struggle to capture the complex geometric features of the prostate.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To enhance the detection and segmentation performance of prostate MR images by addressing limitations in spatial feature extraction and static focusing mechanisms present in conventional YOLO models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We propose YOLO-D, an enhanced YOLOv8-based model integrating a Deformable Convolution (DConv) module to better capture fine-grained image details and improve geometric adaptability. Additionally, the Wise-IoU loss function is employed to introduce a dynamic and non-monotonic focusing mechanism, effectively reducing inter-class interference and enhancing localization accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>YOLO-D was evaluated on the publicly available ProstateX dataset using precision, recall, average precision (AP), and F1 score as evaluation metrics. For detection, it achieved 93.4% precision, 91.2% recall, 94.7% AP, and an F1 score of 0.922. For segmentation, YOLO-D achieved 90.7% precision, 88.6% recall, 91.1% AP, and an F1 score of 0.897\u2014consistently outperforming the baseline YOLOv8.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>By incorporating DConv and Wise-IoU, YOLO-D offers a robust and efficient solution for automatic prostate zone analysis, with promising potential in real-time clinical imaging applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/09287329251392399","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T15:57:11Z","timestamp":1762358231000},"page":"246-260","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Research on automatic detection and segmentation of prostate zones based on YOLO-D"],"prefix":"10.1177","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1536-9192","authenticated-orcid":false,"given":"Xiaodong","family":"Wang","sequence":"first","affiliation":[{"name":"Kangda College of Nanjing Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2422-1786","authenticated-orcid":false,"given":"Rui","family":"Feng","sequence":"additional","affiliation":[{"name":"Nanjing Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Xu","sequence":"additional","affiliation":[{"name":"Kangda College of Nanjing Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2071-7444","authenticated-orcid":false,"given":"Chuanbing","family":"Wang","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital With Nanjing Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9702-6316","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9464-378X","authenticated-orcid":false,"given":"Chang","family":"Gao","sequence":"additional","affiliation":[{"name":"Kangda College of Nanjing Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Tan","sequence":"additional","affiliation":[{"name":"Kangda College of Nanjing Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,11,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-021-01010-9"},{"key":"e_1_3_2_3_2","first-page":"1401","article-title":"Segmentation of prostate zones on a novel MRI database using mask R-CNN: an implementation on PACS system","volume":"39","author":"Gurkan C","year":"2024","unstructured":"Gurkan C, Budak A, Karatas H, et\u00a0al. 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