# Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images

arXiv: https://arxiv.org/abs/2312.05887 · HTML: https://mlsanity.com/paper/2312.05887/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 63 references that are in the map.
- First posted on arXiv 10 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [Semi-supervised Medical Image Segmentation through Dual-task Consistency](https://mlsanity.com/paper/2009.04448/) (2020)
- [A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology](https://mlsanity.com/paper/1910.01877/) (2019)
- [A High-Order Scheme for Image Segmentation via a modified Level-Set method](https://mlsanity.com/paper/1812.03026/) (2018)

## Built on by (most cited first, 1 of 1)

- [AI-based modular warning machine for risk identification in proximity healthcare](https://mlsanity.com/paper/2506.15823/) (2025)

## Abstract (arXiv)

The analysis of the psoas muscle in morphological and functional imaging has proved to be an accurate approach to assess sarcopenia, i.e. a systemic loss of skeletal muscle mass and function that may be correlated to multifactorial etiological aspects. The inclusion of sarcopenia assessment into a radiological workflow would need the implementation of computational pipelines for image processing that guarantee segmentation reliability and a significant degree of automation. The present study utilizes three-dimensional numerical schemes for psoas segmentation in low-dose X-ray computed tomography images. Specifically, here we focused on the level set methodology and compared the performances of two standard approaches, a classical evolution model and a three-dimension geodesic model, with the performances of an original first-order modification of this latter one. The results of this analysis show that these gradient-based schemes guarantee reliability with respect to manual segmentation and that the first-order scheme requires a computational burden that is significantly smaller than the one needed by the second-order approach.