{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T07:56:24Z","timestamp":1774079784187,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T00:00:00Z","timestamp":1645056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The anterior cruciate ligament (ACL) is one of the main stabilizer parts of the knee. ACL injury leads to causes of osteoarthritis risk. ACL rupture is common in the young athletic population. Accurate segmentation at an early stage can improve the analysis and classification of anterior cruciate ligaments tears. This study automatically segmented the anterior cruciate ligament (ACL) tears from magnetic resonance imaging through deep learning. The knee mask was generated on the original Magnetic Resonance (MR) images to apply a semantic segmentation technique with convolutional neural network architecture U-Net. The proposed segmentation method was measured by accuracy, intersection over union (IoU), dice similarity coefficient (DSC), precision, recall and F1-score of 98.4%, 99.0%, 99.4%, 99.6%, 99.6% and 99.6% on 11451 training images, whereas on the validation images of 3817 was, respectively, 97.7%, 93.8%,96.8%, 96.5%, 97.3% and 96.9%. We also provide dice loss of training and test datasets that have remained 0.005 and 0.031, respectively. The experimental results show that the ACL segmentation on JPEG MRI images with U-Nets achieves accuracy that outperforms the human segmentation. The strategy has promising potential applications in medical image analytics for the segmentation of knee ACL tears for MR images.<\/jats:p>","DOI":"10.3390\/s22041552","type":"journal-article","created":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T20:26:41Z","timestamp":1645129601000},"page":"1552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3535-8005","authenticated-orcid":false,"given":"Mazhar Javed","family":"Awan","sequence":"first","affiliation":[{"name":"Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia (UTM), Skudai 81310, Johor, Malaysia"},{"name":"Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohd Shafry Mohd","family":"Rahim","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia (UTM), Skudai 81310, Johor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naomie","family":"Salim","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia (UTM), Skudai 81310, Johor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3817-2655","authenticated-orcid":false,"given":"Amjad","family":"Rehman","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Data Analytics Laboratory, College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9356-1186","authenticated-orcid":false,"given":"Begonya","family":"Garcia-Zapirain","sequence":"additional","affiliation":[{"name":"eVIDA Lab, University of Deusto, 48007 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ajdaroski, M., Tadakala, R., Nichols, L., and Esquivel, A. 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