{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T13:31:58Z","timestamp":1790170318098,"version":"4.1.0"},"reference-count":23,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007847","name":"Natural Science Foundation of Jilin Province","doi-asserted-by":"publisher","award":["20220101128JC"],"award-info":[{"award-number":["20220101128JC"]}],"id":[{"id":"10.13039\/100007847","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007847","name":"Natural Science Foundation of Jilin Province","doi-asserted-by":"publisher","award":["20220101128JC"],"award-info":[{"award-number":["20220101128JC"]}],"id":[{"id":"10.13039\/100007847","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>In medical imaging diagnosis, accurate segmentation of the knee joint can help doctors better observe and diagnose lesions, thereby improving diagnostic accuracy and treatment effectiveness. Vision Mamba mainly relies on the State Space Model (SSM) for feature modeling, which excels at capturing global contextual information but cannot capture local texture features. Moreover, features of different scales are not effectively integrated, resulting in the model\u2019s weak segmentation ability on small-scale tissues (such as cartilage areas). To this end, this study proposed a novel multi-scale Vision Mamba Unet (VM-Unet) framework named MSPF-VM-Unet to perform the segmentation on the femur, tibia, femoral cartilage, and tibial cartilage in knee MRI images. The proposed MSPF-VM-Unet extends VM-Unet by introducing a designed multi-scale pyramid feature extraction network named MPSK, which synergizes multi-resolution feature extraction with channel-space attention. MPSK network enhances multi-scale local feature extraction through Selective Kernel (SK) convolution and pyramid pooling. The network merges the overall context information extracted by the Vision Mamba encoder to achieve the coordinated optimization of a multi-scale hierarchical feature fusion mechanism and global long-range dependency modeling. The results of the comparative experiments on the OAI-ZIB dataset indicate that MSPF-VM-Unet significantly improves the boundary accuracy and regional consistency of the MRI tibiofemoral joint tissue structure.<\/jats:p>","DOI":"10.1371\/journal.pone.0330740","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T17:29:53Z","timestamp":1756402193000},"page":"e0330740","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["VM-Unet enhanced with multi-scale pyramid feature extraction for segmentation of tibiofemoral joint tissues from knee MRI"],"prefix":"10.1371","volume":"20","author":[{"given":"Xin","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yupeng","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huimin","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4387-4907","authenticated-orcid":true,"given":"Xiaodong","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"pone.0330740.ref001","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.media.2018.11.009","article-title":"Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative","volume":"52","author":"F Ambellan","year":"2019","journal-title":"Med Image Anal"},{"issue":"7","key":"pone.0330740.ref002","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1016\/j.joca.2007.01.011","article-title":"In vivo T(1rho) and T(2) mapping of articular cartilage in osteoarthritis of the knee using 3 T MRI","volume":"15","author":"X Li","year":"2007","journal-title":"Osteoarthritis Cartilage"},{"key":"pone.0330740.ref003","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-40763-5_31","article-title":"Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network.","volume-title":"International conference on medical image computing and computer-assisted intervention","author":"A Prasoon","year":"2013"},{"key":"pone.0330740.ref004","doi-asserted-by":"crossref","DOI":"10.1109\/ISBI.2018.8363705","article-title":"Automatic knee cartilage segmentation using fully volumetric convolutional neural networks for evaluation of osteoarthritis.","volume-title":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","author":"A Raj","year":"2018"},{"issue":"7","key":"pone.0330740.ref005","article-title":"Knee cartilage segmentation using improved U-Net","volume":"14","author":"N Waqas","year":"2023","journal-title":"Int J Adv Comput Sci Appl"},{"key":"pone.0330740.ref006","doi-asserted-by":"crossref","DOI":"10.1016\/j.joca.2021.02.454","article-title":"Toward accurate MRI bone and cartilage segmentation in small data sets via an improved mask RCNN: data from the osteoarthritis initiative","volume":"29","author":"B Felfeliyan","year":"2021","journal-title":"Osteoarthritis Cartilage"},{"key":"pone.0330740.ref007","unstructured":"Daydar A, Pramanick A, Sur A, Kanagaraj S. 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