# Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1808.03844 · HTML: https://mlsanity.com/paper/1808.03844/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2018.

## Built on by (most cited first, 3 of 3)

- [Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective](https://mlsanity.com/paper/2207.06551/) (2022)
- [Automated segmentation of 3-D body composition on computed tomography](https://mlsanity.com/paper/2112.08968/) (2021)
- [Reliable Radiologic Skeletal Muscle Area Assessment -- A Biomarker for Cancer Cachexia Diagnosis](https://mlsanity.com/paper/2503.16556/) (2025)

## Abstract (arXiv)

The amounts of muscle and fat in a person's body, known as body composition, are correlated with cancer risks, cancer survival, and cardiovascular risk. The current gold standard for measuring body composition requires time-consuming manual segmentation of CT images by an expert reader. In this work, we describe a two-step process to fully automate the analysis of CT body composition using a DenseNet to select the CT slice and U-Net to perform segmentation. We train and test our methods on independent cohorts. Our results show Dice scores (0.95-0.98) and correlation coefficients (R=0.99) that are favorable compared to human readers. These results suggest that fully automated body composition analysis is feasible, which could enable both clinical use and large-scale population studies.