Deep-learning pipeline for automated skeletal muscle segmentation and sarcopenia detection.
Authors
Affiliations (8)
Affiliations (8)
- Department of Radiodiagnosis, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India. [email protected].
- Department of Radiodiagnosis, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India.
- Department of Gastroenterology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India.
- Department of GI Surgery, HBP and Liver Transplantation, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India.
- Department of General Surgery, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India.
- Department of Clinical Hematology and Medical Oncology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160 012, India.
- Department of Radiodiagnosis, Sanjay Gandhi Post Graduate Institute of Medical Sciences,, Lucknow, 226 014, India.
- Department of Radiodiagnosis, Institute of Medical Sciences, Banaras Hindu University, Varanasi, 221 005, India.
Abstract
Sarcopenia, characterized by progressive skeletal muscle loss, is associated with poor outcomes in various diseases. Traditional methods for assessing muscle cross-sectional area using computed tomography (CT) scans are manual, time-consuming and prone to variability. This study comprehensively validates a deep-learning (DL) pipeline for accurate and reproducible sarcopenia detection on computed tomography across diverse disease abdominal conditions and imaging protocols. We utilized the publicly available Sparsely Annotated Region and Organ Segmentation (SAROS) CT dataset (n = 550 CT scans, 6516 slices) for model training. Testing was conducted on 601 CT scans from public (SAROS, Cancer Imaging Archive [TCIA] , WAW-TACE) and in-house multi-center datasets representing varied clinical conditions (acute pancreatitis, inflammatory bowel disease, gallbladder cancer and distal bile duct obstruction). The implemented pipeline integrated TotalSegmentator for L3 vertebral segmentation, automated L3 slice extraction and skeletal muscle segmentation using nnU-Net. Performance evaluation included expert qualitative scoring, Dice scores, intersection over union (IoU) and diagnostic accuracy metrics for sarcopenia detection. The DL pipeline demonstrated consistent segmentation accuracy across diverse datasets, with mean Dice scores ranging from 0.9287 to 0.9701 and mean IoU values up to 0.9423. Expert evaluation confirmed reliable L3 vertebral segmentation (78%-85% rated as complete) and skeletal muscle segmentation (90%-92.6% rated as excellent). Sarcopenia detection was consistent across varied patient populations, with sensitivity (0.94-0.97), specificity (0.84-0.97) and AUC values up to 0.92. Importantly, sub-group analysis confirmed comparable performance across varying disease conditions, CT protocols, contrast usage and radiation doses. This study demonstrates that a deep-learning pipeline can achieve consistent and reliable performance for skeletal muscle segmentation and sarcopenia detection across heterogeneous abdominal CT protocols and diverse clinical conditions.