# Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis

arXiv: https://arxiv.org/abs/2502.09779 · HTML: https://mlsanity.com/paper/2502.09779/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 139 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024)
- [The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge](https://mlsanity.com/paper/1912.01054/) (2019)
- [MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation](https://mlsanity.com/paper/2303.09975/) (2023)
- [DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation](https://mlsanity.com/paper/1506.06448/) (2015)
- [How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model](https://mlsanity.com/paper/2404.09957/) (2024)
- [Segment anything model 2: an application to 2D and 3D medical images](https://mlsanity.com/paper/2408.00756/) (2024)
- [SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI](https://mlsanity.com/paper/2401.12974/) (2024)
- [Fully-automated Body Composition Analysis in Routine CT Imaging Using 3D Semantic Segmentation Convolutional Neural Networks](https://mlsanity.com/paper/2002.10776/) (2020)
- [Convolutional Neural Networks Rarely Learn Shape for Semantic Segmentation](https://mlsanity.com/paper/2305.06568/) (2023)
- [Enhanced Muscle and Fat Segmentation for CT-Based Body Composition Analysis: A Comparative Study](https://mlsanity.com/paper/2401.05294/) (2024)
- [Automatic segmentation of CT images for ventral body composition analysis](https://mlsanity.com/paper/2009.01965/) (2020)

## Built on by (most cited first, 5 of 5)

- [Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2](https://mlsanity.com/paper/2505.01854/) (2025)
- [Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface](https://mlsanity.com/paper/2505.22511/) (2025)
- [SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI](https://mlsanity.com/paper/2506.22467/) (2025)
- [Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery](https://mlsanity.com/paper/2506.11996/) (2025)
- [LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification](https://mlsanity.com/paper/2605.30829/) (2026)

## Abstract (arXiv)

Body composition assessment using CT images can potentially be used for a number of clinical applications, including the prognostication of cardiovascular outcomes, evaluation of metabolic health, monitoring of disease progression, assessment of nutritional status, prediction of treatment response in oncology, and risk stratification for surgical and critical care outcomes. While multiple groups have developed in-house segmentation tools for this analysis, there are very limited publicly available tools that could be consistently used across different applications. To mitigate this gap, we present a publicly accessible, end-to-end segmentation and feature calculation model specifically for CT body composition analysis. Our model performs segmentation of skeletal muscle, subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) across the chest, abdomen, and pelvis area in axial CT images. It also provides various body composition metrics, including muscle density, visceral-to-subcutaneous fat (VAT/SAT) ratio, muscle area/volume, and skeletal muscle index (SMI), supporting both 2D and 3D assessments. To evaluate the model, the segmentation was applied to both internal and external datasets, with body composition metrics analyzed across different age, sex, and race groups. The model achieved high dice coefficients on both internal and external datasets, exceeding 89% for skeletal muscle, SAT, and VAT segmentation. The model outperforms the benchmark by 2.10% on skeletal muscle and 8.6% on SAT compared to the manual annotations given by the publicly available dataset. Body composition metrics show mean relative absolute errors (MRAEs) under 10% for all measures. Our model with weights is publicly available at https://github.com/mazurowski-lab/CT-Muscle-and-Fat-Segmentation.git.