ArticleBiomedical engineering online2026
Comparison between automated and manual segmentation in computed tomography for body composition analysis.
Article in Biomedical engineering online, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- AutoLMISeg: A Fully Automated Pipeline for 3D Lumbar Muscle Volumetry and Densitometry on CT - Technical Validation Against an Operator-Driven Workflow.Journal of imaging informatics in medicine · 2026Article
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4 authors.
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Abstract
BACKGROUND AND
objectivesBody 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.
methodsThis 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 cm
conclusionsIACC 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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