{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T04:49:12Z","timestamp":1782103752059,"version":"3.54.5"},"reference-count":31,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":19,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["bjui-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BJUI Compass"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aimed to develop and evaluate MUSeg\u2010PSV, a real\u2010time deep\u2010learning framework for segmentation of the prostate gland and seminal vesicles on 29\u2010MHz micro\u2010ultrasound (MicroUS), intended to support anatomical orientation during MicroUS\u2010guided prostate biopsy and local staging of prostate cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patients and methods<\/jats:title>\n                    <jats:p>MicroUS data from 14 patients (21 full\u2010length videos; 2588 annotated frames) were used to train and test MUSeg\u2010PSV, a lightweight single\u2010stage convolutional model based on YOLOv11s\u2010seg. Two training strategies were compared: a standard configuration with basic augmentations and an augmentation\u2010rich configuration incorporating advanced spatial and photometric transformations. Model performance was assessed on a held\u2010out test set using mean average precision (mAP), dice similarity coefficient (DSC), expected calibration error (ECE) and Brier score. Real\u2010time feasibility was evaluated through inference latency and qualitative assessment of temporal consistency on unseen videos.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The augmentation\u2010rich model outperformed the standard configuration, yielding a 21% relative increase in mAP and a 4.6\u2010fold improvement in seminal vesicle AP (0.183\u2009\u2192\u20090.842). Prostate DSC increased from 0.675 to 0.770 (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20094.4\u2009\u00d7\u200910\n                      <jats:sup>\u22125<\/jats:sup>\n                      ), while seminal vesicle DSC remained robust (0.892 vs. 0.876), reflecting a controlled trade\u2010off with substantially improved SV detection (AP: 0.183\u2009\u2192\u20090.842). Calibration improved markedly, with ECE decreasing from 0.558 to 0.156 and Brier score from 1.000 to 0.196 (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01). Qualitative evaluation confirmed smooth, temporally coherent overlays and consistent delineation of challenging anatomical regions. End\u2010to\u2010end latency (28\u201335\u2009ms\/frame; ~35 fps) demonstrated compatibility with real\u2010time clinical deployment.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>MUSeg\u2010PSV enables reliable, real\u2010time multiclass segmentation of high\u2010frequency MicroUS, providing stable delineation of the prostate and seminal vesicles at clinically viable frame rates. These results support the potential of AI\u2010assisted MicroUS to enhance operator orientation during MicroUS\u2010guided prostate biopsy and local staging, and to promote standardised identification of seminal vesicles within prostate cancer diagnostic and staging workflows.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/bco2.70211","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T07:48:17Z","timestamp":1779263297000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<i>MUSeg\u2010PSV:<\/i>\n                    A Real\u2010Time Deep\u2010Learning Segmentation of the Prostate Gland and Seminal Vesicles on 29\u2010MHz Micro\u2010Ultrasound"],"prefix":"10.1002","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0399-2395","authenticated-orcid":false,"given":"Ludovica","family":"Cella","sequence":"first","affiliation":[{"name":"Department of Biomedical 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