{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T17:05:38Z","timestamp":1784307938745,"version":"3.55.0"},"reference-count":33,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:00:00Z","timestamp":1776124800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:00:00Z","timestamp":1776124800000},"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":["BJU International"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop a deep\u2010learning segmentation model for bladder neck dissection during robot\u2010assisted radical prostatectomy (RARP) and evaluate its effectiveness in improving anatomical recognition in surgical videos across surgeons with varying levels of experience.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Subjects, Patients and Methods<\/jats:title>\n                    <jats:p>From April 2022 to March 2023, we collected 25 RARP procedures at Fujita Health University, specifically extracting bladder neck dissection segments. Three architectures (U\u2010Net, U\u2010Net++, and DeepLabv3+) were trained on 979 annotated frames from 10 RARP procedures performed by a single expert surgeon. The best\u2010performing model was adopted as the base model for two\u2010step, entropy\u2010based active learning. Step one included selecting informative frames from the same videos used for the base model, with finer temporal sampling, to construct the prototype model. In step two, diverse procedures from additional surgeons were used to build the final model. Segmentation performance was evaluated using Intersection over Union (IoU; mean IoU [mIoU]). Clinical utility was assessed by quantifying the recognition of anatomical structures with and without guidance from the segmentation model among seven urologists (two expert surgeons and five novice surgeons). Anatomical recognition was evaluated by comparing annotation time and the average IoU of the bladder and prostate (bpIoU).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>DeepLabv3+ with LogCosh loss achieved the highest performance (mIoU\u2009=\u20090.815, 95% confidence interval [CI] 0.699\u20130.931) and was adopted as the base model. Active learning improved generalisability, increasing mIoU on the novice evaluation set from 0.709 (95% CI 0.584\u20130.833) to 0.713 (95% CI 0.586\u20130.839). Regarding the assessment of clinical utility, among novice surgeons, model guidance significantly reduced annotation time by 28\u2009s and improved annotation quality of bpIoU by 0.124. No significant changes were observed among expert surgeons.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The developed segmentation model enhanced the speed and performance of anatomical recognition, particularly for novice surgeons, and may support surgical education.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/bju.70287","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T06:17:13Z","timestamp":1776233833000},"page":"234-243","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep\u2010learning segmentation to guide bladder neck recognition in robot\u2010assisted radical prostatectomy"],"prefix":"10.1111","volume":"138","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7578-1132","authenticated-orcid":false,"given":"Yoshinari","family":"Muto","sequence":"first","affiliation":[{"name":"Department of Urology Fujita Health University School of Medicine  Toyoake Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8455-7364","authenticated-orcid":false,"given":"Kenji","family":"Zennami","sequence":"additional","affiliation":[{"name":"Department of Urology Fujita Health University School of Medicine  Toyoake Japan"},{"name":"Department of Urology Nagoya University Graduate School of Medicine  Nagoya Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kota","family":"Yagi","sequence":"additional","affiliation":[{"name":"Department of Urology Fujita Health University School of Medicine  Toyoake Japan"},{"name":"Medical Office, Japan Ground Self\u2010Defense Force Camp Moriyama  Nagoya Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masashi","family":"Takenaka","sequence":"additional","affiliation":[{"name":"Department of Urology Fujita Health University School of Medicine  Toyoake Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4766-2074","authenticated-orcid":false,"given":"Kiyoshi","family":"Takahara","sequence":"additional","affiliation":[{"name":"Department of Urology Fujita Health University School of Medicine  Toyoake Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryoichi","family":"Shiroki","sequence":"additional","affiliation":[{"name":"Department of Urology Fujita Health University School of Medicine  Toyoake Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,14]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"FerlayJ LamF LaversanneMet\u00a0al.Global cancer observatory: cancer today.2024"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2019.08.005"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/JU.0000000000000147"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0253081"},{"key":"e_1_2_9_6_1","first-page":"2037","article-title":"The learning curves of major laparoscopic and robotic procedures in urology: a systematic review","volume":"109","author":"Chahal B","year":"2023","journal-title":"Int J Surg"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/CU9.0000000000000119"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.2147\/JMDH.S482757"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_9_10_1","first-page":"3523","article-title":"Image segmentation using deep learning: a survey","volume":"44","author":"Minaee S","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.euros.2024.02.005"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.urology.2022.12.006"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/4235.585893"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-023-10224-5"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/584091.584093"},{"key":"e_1_2_9_16_1","first-page":"427","volume-title":"DAGM German Conference on Pattern Recognition","author":"Mittal S","year":"2023"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-43092-z"},{"key":"e_1_2_9_18_1","unstructured":"WadaK.Labelme: Image polygonal annotation with python.GitHub.2016. 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