{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T08:48:51Z","timestamp":1792226931390,"version":"4.4.0"},"reference-count":20,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Health & Welfare, Republic of Korea","award":["HI21C0196"],"award-info":[{"award-number":["HI21C0196"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background\/Purpose<\/jats:title>\n<jats:p>The use of MRI as a diagnostic tool has gained popularity in the field of orthopedics. Although 3-dimensional (3D) MRI offers more intuitive visualization and can better facilitate treatment planning than 2-dimensional (2D) MRI, manual segmentation for 3D visualization is time-consuming and lacks reproducibility. Recent advancements in deep learning may provide a solution to this problem through the process of automatic segmentation. The purpose of this study was to develop automated semantic segmentation on 2D MRI images of rotator cuff tears by using a convolutional neural network to visualize 3D models of related anatomic structures.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>MRI scans from 56 patients with rotator cuff tears (T2 Linear Coronal MRI; 3.0T, 512 mm \u00d7 512 mm, and 2.5-mm slice thickness) were collected. Segmentation masks for the cuff tendon, muscle, bone, and cartilage were obtained by four orthopedic shoulder surgeons, and these data were revised by a shoulder surgeon with more than 20 years\u2019 experience. We performed 2D and 3D segmentation using nnU-Net with secondary labels for reducing false positives. Final validation was performed in an external T2 MRI dataset (10 cases) acquired from other institutions. The Dice Similarity Coefficient (DSC) was used to validate segmentation quality.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>The use of 3D nnU-Net with secondary labels to reduce false positives achieved satisfactory results, even with a limited amount of data. The DSCs (mean \u00b1 SD) of the cuff tendon, muscle, bone, and cartilage in the internal test set were 80.7% \u00b1 9.7%, 85.8% \u00b1 8.6%, 97.8% \u00b1 0.6%, and 80.8% \u00b1 15.1%, respectively. In external validation, the DSC of the tendon segmentation was 82.74\u00b15.2%.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>Automated segmentation using 3D U-Net produced acceptable accuracy and reproducibility. This method could provide rapid, intuitive visualization that can significantly facilitate the diagnosis and treatment planning in patients with rotator cuff tears.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0274075","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T17:49:49Z","timestamp":1665424189000},"page":"e0274075","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":21,"title":["Can deep learning reduce the time and effort required for manual segmentation in 3D reconstruction of MRI in rotator cuff tears?"],"prefix":"10.1371","volume":"17","author":[{"given":"Hyojune","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keewon","family":"Shin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hoyeon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eui-sup","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seok Won","family":"Chung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6181-9621","authenticated-orcid":true,"given":"Kyoung Hwan","family":"Koh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3438-2217","authenticated-orcid":true,"given":"Namkug","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"issue":"12","key":"pone.0274075.ref001","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1016\/j.jse.2017.07.012","article-title":"Cost-effectiveness of magnetic resonance imaging versus ultrasound for the detection of symptomatic full-thickness supraspinatus tendon tears","volume":"26","author":"S Gyftopoulos","year":"2017","journal-title":"J Shoulder Elbow Surg"},{"issue":"2","key":"pone.0274075.ref002","first-page":"2325967119900356","article-title":"Detecting Rotator Cuff Tears: A Network Meta-analysis of 144 Diagnostic Studies","volume":"8","author":"F 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