ArticleBMJ open2024
Development and validation of a reliable method for automated measurements of psoas muscle volume in CT scans using deep learning-based segmentation: a cross-sectional study.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Incremental diagnostic value of multiregional single-slice CT muscle areas over L3 for sarcopenia: a deep learning-based segmentation study.Skeletal radiology · 2026Article
- Recurrence After Rectopexy: Insights From Magnetic Resonance Defecography.Diseases of the colon and rectum · 2026Article
- Deep Learning-Based 3D and 2D Approaches for Skeletal Muscle Segmentation on Low-Dose CT Images.Journal of imaging informatics in medicine · 2026Article
- Assessing osteosarcopenia using automated multi-structure-segmentation and Hounsfield unit-based analysis of lumbar muscles and vertebral bodies on computed tomography.Scientific reports · 2026Article
- Psoas muscle volume as a diagnostic indicator for sarcopenia: criteria development and comparison with traditional diagnostic approaches.Aging clinical and experimental research · 2026Article
- Sarcopenia is a bad harbinger of cancer-related survival in rectal cancer.Annals of medicine · 2025Article
- Sarcopenia-A Valuable Imaging Biomarker for Disease Progression in Patients With Primary Sclerosing Cholangitis (PSC)?Liver international : official journal of the International Association for the Study of the Liver · 2025Article
- Effect of subcutaneous fat on long-term survival after surgery for stage I-III breast cancer as determined by computed tomography.Frontiers in oncology · 2025Article
- Skeletal muscle size and quality in healthy kidney donors, normal range and clinical associations.Scientific reports · 2024Article
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Authors and funding
6 authors.
Funding
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Abstract
objectivesWe aimed to develop an automated method for measuring the volume of the psoas muscle using CT to aid sarcopenia research efficiently.
methodsWe used a data set comprising the CT scans of 520 participants who underwent health check-ups at a health promotion centre. We developed a psoas muscle segmentation model using deep learning in a three-step process based on the nnU-Net method. The automated segmentation method was evaluated for accuracy, reliability, and time required for the measurement.
resultsThe Dice similarity coefficient was used to compare the manual segmentation with automated segmentation; an average Dice score of 0.927 ± 0.019 was obtained, with no critical outliers. Our automated segmentation system had an average measurement time of 2 min 20 s ± 20 s, which was 48 times shorter than that of the manual measurement method (111 min 6 s ± 25 min 25 s).
conclusionWe have successfully developed an automated segmentation method to measure the psoas muscle volume that ensures consistent and unbiased estimates across a wide range of CT images.
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