Related Experiment Video For Automated body composition analysis
Updated: Jun 18, 2025
Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
Published on: March 21, 2021
Validation of an automated segmentation method for body composition analysis in colorectal cancer patients using
Nadira R Querido1, Martijn J L Bours1, Ralph Brecheisen2
1Department of Epidemiology, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands.
Background & Aims:
Several automated programs have been developed to facilitate body composition analysis of images from abdominal computed tomography (CT) scans. External validation in patients with colorectal cancer is necessary for use in research and clinical practice. Our aim was to validate an automatic method (AutoMATiCA) of segmenting CT images at the third lumbar level (L3) from patients with colorectal cancer, by comparing with manual segmentation.
Methods:
Diagnostic abdominal CT scans of consecutive patients with stage I-III colorectal cancer were analysed to measure cross-sectional areas and tissue densities of skeletal muscle and intra-muscular, visceral, and subcutaneous adipose tissue. Trained analysts performed manual segmentation of L3 CT images using SliceOmatic. Automatic segmentation was performed using AutoMATiCA, an open-source software. The Dice similarity coefficient (DSC) was calculated to assess segmentation accuracy. Agreement of automatic with manual segmentation was evaluated using intra-class correlation coefficients (ICCs) and Bland-Altman plots with limits of agreement.
Results:
A total of 292 scans were included, of which 62% were from male patients. The agreement of AutoMATiCA with the manual segmentation was excellent, with median DSC values ranging from 0.900 to 0.991 and ICCs above 0.95 for all segmented areas. No systematic deviations were observed in Bland-Altman plots for all segmented areas, with overall narrow limits of agreement.
Conclusions:
AutoMATiCA provides an accurate segmentation of abdominal CT images from patients with colorectal cancer. Our findings support its use as a highly efficient automated tool for body composition analysis in research and potentially also in clinical practice.
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