# Fully-automated Body Composition Analysis in Routine CT Imaging Using 3D Semantic Segmentation Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2002.10776 · HTML: https://mlsanity.com/paper/2002.10776/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding](https://mlsanity.com/paper/2210.03416/) (2022)
- [Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling](https://mlsanity.com/paper/2306.03934/) (2023)
- [Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines](https://mlsanity.com/paper/2307.13375/) (2023)
- [Current State of Community-Driven Radiological AI Deployment in Medical Imaging](https://mlsanity.com/paper/2212.14177/) (2022)
- [Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis](https://mlsanity.com/paper/2502.09779/) (2025)
- [Enhanced Muscle and Fat Segmentation for CT-Based Body Composition Analysis: A Comparative Study](https://mlsanity.com/paper/2401.05294/) (2024)
- [AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation](https://mlsanity.com/paper/2308.08172/) (2023)
- [Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries](https://mlsanity.com/paper/2306.17555/) (2023)
- [SALT: Introducing a Framework for Hierarchical Segmentations in Medical Imaging using Softmax for Arbitrary Label Trees](https://mlsanity.com/paper/2407.08878/) (2024)
- [Automated segmentation of 3-D body composition on computed tomography](https://mlsanity.com/paper/2112.08968/) (2021)
- [AI-Driven Automated Tool for Abdominal CT Body Composition Analysis in Gastrointestinal Cancer Management](https://mlsanity.com/paper/2503.07248/) (2025)
- [Reliable Radiologic Skeletal Muscle Area Assessment -- A Biomarker for Cancer Cachexia Diagnosis](https://mlsanity.com/paper/2503.16556/) (2025)

## Abstract (arXiv)

Body tissue composition is a long-known biomarker with high diagnostic and prognostic value in cardiovascular, oncological and orthopaedic diseases, but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging. Therefore an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known hounsfield unit limits. The Sorensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99. Our results show that fully-automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analysing body composition in the clinical routine.