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Fully automated deep-learning section-based muscle segmentation from CT images for sarcopenia assessment
S Islam1, F Kanavati2, Z Arain2
1Comprehensive Cancer Imaging Centre, Division of Cancer, Dept of Cancer and Surgery, Faculty of Medicine, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NS, UK; Dept of Radiology, Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Rd, London, W12 0NS, UK.
This study introduces a deep learning method to automatically measure muscle area from CT scans for sarcopenia assessment. The fully automated approach efficiently screens for frailty using standard abdominal CT images.
Area of Science:
- Radiology
- Artificial Intelligence
- Gerontology
Background:
- Sarcopenia, a key indicator of frailty, is often assessed using muscle area measurements.
- Standard computed tomography (CT) scans of the abdomen present an opportunity for opportunistic sarcopenia screening.
- Current methods for muscle area quantification can be time-consuming and require manual input.
Purpose of the Study:
- To develop and validate a fully automated deep learning (DL) approach for measuring muscle area.
- To enable sarcopenia assessment from standard-of-care abdominal CT images without exclusion criteria.
- To facilitate opportunistic screening for frailty.
Main Methods:
- A retrospective study utilizing 1,070 training and 31 testing abdominal CT images.
- A two-stage fully convolutional neural network (FCNN) pipeline: section detection followed by muscle segmentation.
- Segmentation focused on L3 vertebral level muscles (erector spinae, psoas, rectus abdominus).
Main Results:
- The FCNN pipeline achieved high accuracy in muscle segmentation, with Dice overlaps of 0.96 ± 0.02 for combined muscle area.
- Excellent correlation for muscle attenuation (R² = 0.95) and area (R² = 0.98) on hold-out test cases.
- Fully automated processing completed in under 1 second per CT examination, with no statistical difference from manual segmentation.
Conclusions:
- A FCNN pipeline accurately and efficiently automates muscle segmentation at the L3 level from unselected abdominal CT volumes.
- This automated approach requires no manual processing, making it suitable for opportunistic frailty screening.
- The method shows promise as a generalizable tool for widespread clinical application.
