Comparison between automated and manual segmentation in computed tomography for body composition analysis

TL;DR

This study compares automated and manual methods for analyzing body composition in CT scans, finding that the automated method is faster and accurate for fat tissues but less consistent for muscle.

Contribution

The study introduces and evaluates IACC, an automated tool for CT-based body composition analysis, demonstrating its efficiency and accuracy compared to manual methods.

Findings

IACC reduced analysis time from 25–30 minutes to about 5 minutes per scan.

High agreement was found for adipose tissues (SAT and VAT) with ICC values above 0.93.

Skeletal muscle measurements showed higher variability compared to adipose tissues.

Abstract

Body composition (BC) assessment by computed tomography (CT) at the level of the third lumbar vertebra (L3) is considered the gold standard for evaluating muscle and adipose compartments. However, manual segmentation is time-consuming and impractical for large data sets. This study aimed to compare the automated method, Identificação Automatizada da Composição Corporal (IACC), with manual segmentation performed using 3D Slicer.

This retrospective cross-sectional study included 126 participants, each contributing one single axial CT slice at the L3 level, obtained from routine clinical CT scans acquired between November 2023 and January 2025. Manual segmentations were performed in 3D Slicer by a trained evaluator and validated by an experienced radiologist. The IACC automatically identified the L3 level and segmented skeletal muscle (MUSCLE), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT). Analyses included intra- and inter-rater reproducibility (ICC, Dice coefficient), Pearson correlation, agreement (ICC), and Bland–Altman plots.

Results

The mean analysis time was approximately 5 min per scan with IACC, compared with 25–30 min using the manual method. Intra-rater reproducibility was excellent (ICC > 0.99), and inter-rater agreement demonstrated high Dice coefficients for SAT (0.982), skeletal muscle (0.940), and VAT (0.932). Concordance between methods was high for SAT (ICC = 0.932) and VAT (ICC = 0.958), and good for skeletal muscle (ICC = 0.784), all with Pearson’s r > 0.97. Bland–Altman analysis showed minimal mean bias for VAT (− 0.35 cm2) and small variability for SAT, indicating excellent agreement for adipose tissue compartments. In contrast, skeletal muscle presented greater disagreement, with a larger negative bias and wider limits of agreement, reflecting higher variability in automated measurements.

IACC demonstrated high accuracy and efficiency for automated segmentation of SAT and muscle, substantially reducing analysis time compared to manual segmentation. Although greater variability was observed for skeletal muscle measurements, the tool shows promise for use in large-scale population studies and potential clinical applications, particularly, where rapid and standardized analyses are required.

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