ArticleProceedings of SPIE--the International Society for Optical Engineering2020
Abdominal muscle segmentation from CT using a convolutional neural network.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Association of computed tomography (CT)-derived muscle area and density at multiple sites with 10-year fracture risk in older men.JBMR plus · 2026Article
- Associations of CT Muscle Area and Density With Functional Outcomes and Mortality Across Anatomical Regions in Older Men.Journal of the American Geriatrics Society · 2025Article
- Automated segmentation of trunk musculature with a deep CNN trained from sparse annotations in radiation therapy patients with metastatic spine disease: an observational study.Frontiers in bioengineering and biotechnology · 2025Article
- Accelerated muscle mass estimation from CT images through transfer learning.BMC medical imaging · 2024Article
- A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment.Quantitative imaging in medicine and surgery · 2023Article
- Direct quantification of epistemic and aleatoric uncertainty in 3D U-net segmentation.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Transfer learning for data-efficient abdominal muscle segmentation with convolutional neural networks.Medical physics · 2022Article
- Validation for measurements of skeletal muscle areas using low-dose chest computed tomography.Scientific reports · 2022Article
- The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer.Disease markers · 2022Review
- Deep-Learning Assessed Muscular Hypodensity Independently Predicts Mortality in DLBCL Patients Younger Than 60 Years.Cancers · 2021Article
- Deep Learning-Based Abdominal Muscle Segmentation on CT Images of Surgical Patient Populations.Proceedings of SPIE--the International Society for Optical EngineeringArticle
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Authors and funding
7 authors.
Funding
Abstract
CT is widely used for diagnosis and treatment of a variety of diseases, including characterization of muscle loss. In many cases, changes in muscle mass, particularly abdominal muscle, indicate how well a patient is responding to treatment. Therefore, physicians use CT to monitor changes in muscle mass throughout the patient's course of treatment. In order to measure the muscle, radiologists must segment and review each CT slice manually, which is a time-consuming task. In this work, we present a fully convolutional neural network (CNN) for the segmentation of abdominal muscle on CT. We achieved a mean Dice similarity coefficient of 0.92, a mean precision of 0.93, and a mean recall of 0.91 in an independent test set. The CNN-based segmentation method can provide an automatic tool for the segmentation of abdominal muscle. As a result, the time required to obtain information about changes in abdominal muscle using the CNN takes a fraction of the time associated with manual segmentation methods and thus can provide a useful tool in the clinical application.
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