{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T04:47:26Z","timestamp":1791262046897,"version":"4.1.0"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:00:00Z","timestamp":1657756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AFM-Telethon","award":["#23604"],"award-info":[{"award-number":["#23604"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Purpose: Infiltration of fat into lower limb muscles is one of the key markers for the severity of muscle pathologies. The level of fat infiltration varies in its severity across and within patients, and it is traditionally estimated using visual radiologic inspection. Precise quantification of the severity and spatial distribution of this pathological process requires accurate segmentation of lower limb anatomy into muscle and fat. Methods: Quantitative magnetic resonance imaging (qMRI) of the calf and thigh muscles is one of the most effective techniques for estimating pathological accumulation of intra-muscular adipose tissue (IMAT) in muscular dystrophies. In this work, we present a new deep learning (DL) network tool for automated and robust segmentation of lower limb anatomy that is based on the quantification of MRI\u2019s transverse (T2) relaxation time. The network was used to segment calf and thigh anatomies into viable muscle areas and IMAT using a weakly supervised learning process. A new disease biomarker was calculated, reflecting the level of abnormal fat infiltration and disease state. A biomarker was then applied on two patient populations suffering from dysferlinopathy and Charcot\u2013Marie\u2013Tooth (CMT) diseases. Results: Comparison of manual vs. automated segmentation of muscle anatomy, viable muscle areas, and intermuscular adipose tissue (IMAT) produced high Dice similarity coefficients (DSCs) of 96.4%, 91.7%, and 93.3%, respectively. Linear regression between the biomarker value calculated based on the ground truth segmentation and based on automatic segmentation produced high correlation coefficients of 97.7% and 95.9% for the dysferlinopathy and CMT patients, respectively. Conclusions: Using a combination of qMRI and DL-based segmentation, we present a new quantitative biomarker of disease severity. This biomarker is automatically calculated and, most importantly, provides a spatially global indication for the state of the disease across the entire thigh or calf.<\/jats:p>","DOI":"10.3390\/bioengineering9070315","type":"journal-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T00:17:10Z","timestamp":1657844230000},"page":"315","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Quantification of Intra-Muscular Adipose Infiltration in Calf\/Thigh MRI Using Fully and Weakly Supervised Semantic Segmentation"],"prefix":"10.3390","volume":"9","author":[{"given":"Rula","family":"Amer","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 6139001, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jannette","family":"Nassar","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 6139001, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amira","family":"Trabelsi","sequence":"additional","affiliation":[{"name":"Center for Magnetic Resonance in Biology and Medicine, Aix Marseille University, 13007 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Bendahan","sequence":"additional","affiliation":[{"name":"Center for Magnetic Resonance in Biology and Medicine, Aix Marseille University, 13007 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hayit","family":"Greenspan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 6139001, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2944-6412","authenticated-orcid":false,"given":"Noam","family":"Ben-Eliezer","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 6139001, Israel"},{"name":"Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6139001, Israel"},{"name":"Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY 10016, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1016\/S0140-6736(12)61897-2","article-title":"Muscular dystrophies","volume":"381","author":"Mercuri","year":"2013","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1002\/jmri.23998","article-title":"Comparison of dixon and T1-weighted MR methods to assess the degree of fat infiltration in duchenne muscular dystrophy patients","volume":"38","author":"Wokke","year":"2013","journal-title":"J. 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