ArticleScientific reports2021
Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Correlation Between Paravertebral Muscles Degeneration and Spinopelvic Parameters in Patients With Osteoporotic Vertebral Compression Fractures.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Integrated Assessment of Sarcopenia in Patients with Gastric Cancer Using Deep Learning and Radiomics.Journal of cachexia, sarcopenia and muscle · 2026Article
- AI-assessed sarcopenia as an independent predictor of neoadjuvant chemotherapy outcomes in muscle-invasive bladder cancer.La Radiologia medica · 2026Article
- Automated L3 Skeletal Muscle Segmentation for the Evaluation of Sarcopenia: Development and Independent Validation of an Ensemble-Based 2D nnU-Net Pipeline in a Complex Liver Disease Cohort.Muscles (Basel, Switzerland) · 2026Article
- Impact of data augmentation size on deep learning-based third lumbar vertebra computed tomography skeletal muscle segmentation performance.Quantitative imaging in medicine and surgery · 2026Article
- Impact of sarcopenia on postoperative outcomes and recurrence in patients with Crohn disease: A retrospective cohort study.Medicine · 2026Article
- CT Body Composition Changes Predict Survival in Immunotherapy-Treated Cancer Patients: A Retrospective Cohort Study.Cancers · 2026Article
- Deep-learning pipeline for automated skeletal muscle segmentation and sarcopenia detection.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2026Article
- Temporal Degradation of Skeletal Muscle Quality on CT as a Prognostic Marker in Septic Shock.Diagnostics (Basel, Switzerland) · 2026Article
- Independent validation of the Mosamatic deep learning automated skeletal muscle and adipose tissue segmentation tool in an external Chinese cancer patient cohort.BJR artificial intelligence · 2026Article
- Precision nutrition in gastric cancer: current advances and future directions.Frontiers in nutrition · 2026Review
- Prognostic implications of body composition changes in patients with non-metastatic pancreatic adenocarcinoma treated with mFOLFIRINOX.Frontiers in oncology · 2026Article
- Leveraging Sarcopenia index by automated CT body composition analysis for pan cancer prognostic stratification.NPJ digital medicine · 2025Article
- Implementation of Fully Automated AI-Integrated System for Body Composition Assessment on Computed Tomography for Opportunistic Sarcopenia Screening: Multicenter Prospective Study.JMIR formative research · 2025Article
- Improved muscle and fat segmentation for body composition measures on quantitative CT.International journal of computer assisted radiology and surgery · 2025Article
- ODIASP: An Open-Source Software for Automated SMI Determination-Application to an Inpatient Population.Journal of cachexia, sarcopenia and muscle · 2025Article
- Comparison of Sarcopenia Assessment in Liver Transplant Recipients by Computed Tomography Freehand Region-of-Interest versus an Automated Deep Learning System.Clinical transplantation · 2025Article
- Exchange of Quantitative Computed Tomography Assessed Body Composition Data Using Fast Healthcare Interoperability Resources as a Necessary Step Toward Interoperable Integration of Opportunistic Screening Into Clinical Practice: Methodological Development Study.Journal of medical Internet research · 2025Article
- A randomized controlled trial of a digital lifestyle intervention involving postoperative patients with colorectal cancer.NPJ digital medicine · 2025Article
- Development of an artificial intelligence-based application for the diagnosis of sarcopenia: a retrospective cohort study using the health examination dataset.BMC medical informatics and decision making · 2025Article
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14 authors.
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Abstract
As sarcopenia research has been gaining emphasis, the need for quantification of abdominal muscle on computed tomography (CT) is increasing. Thus, a fully automated system to select L3 slice and segment muscle in an end-to-end manner is demanded. We aimed to develop a deep learning model (DLM) to select the L3 slice with consideration of anatomic variations and to segment cross-sectional areas (CSAs) of abdominal muscle and fat. Our DLM, named L3SEG-net, was composed of a YOLOv3-based algorithm for selecting the L3 slice and a fully convolutional network (FCN)-based algorithm for segmentation. The YOLOv3-based algorithm was developed via supervised learning using a training dataset (n = 922), and the FCN-based algorithm was transferred from prior work. Our L3SEG-net was validated with internal (n = 496) and external validation (n = 586) datasets. Ground truth L3 level CT slice and anatomic variation were identified by a board-certified radiologist. L3 slice selection accuracy was evaluated by the distance difference between ground truths and DLM-derived results. Technical success for L3 slice selection was defined when the distance difference was < 10 mm. Overall segmentation accuracy was evaluated by CSA error and DSC value. The influence of anatomic variations on DLM performance was evaluated. In the internal and external validation datasets, the accuracy of automatic L3 slice selection was high, with mean distance differences of 3.7 ± 8.4 mm and 4.1 ± 8.3 mm, respectively, and with technical success rates of 93.1% and 92.3%, respectively. However, in the subgroup analysis of anatomic variations, the L3 slice selection accuracy decreased, with distance differences of 12.4 ± 15.4 mm and 12.1 ± 14.6 mm, respectively, and with technical success rates of 67.2% and 67.9%, respectively. The overall segmentation accuracy of abdominal muscle areas was excellent regardless of anatomic variation, with CSA errors of 1.38-3.10 cm
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