# Bone Segmentation in Contrast Enhanced Whole-Body Computed Tomography

arXiv: https://arxiv.org/abs/2008.05223 · HTML: https://mlsanity.com/paper/2008.05223/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [Improved distinct bone segmentation in upper-body CT through multi-resolution networks](https://mlsanity.com/paper/2301.13674/) (2023)
- [Ensemble uncertainty as a criterion for dataset expansion in distinct bone segmentation from upper-body CT images](https://mlsanity.com/paper/2208.09216/) (2022)

## Abstract (arXiv)

Segmentation of bone regions allows for enhanced diagnostics, disease characterisation and treatment monitoring in CT imaging. In contrast enhanced whole-body scans accurate automatic segmentation is particularly difficult as low dose whole body protocols reduce image quality and make contrast enhanced regions more difficult to separate when relying on differences in pixel intensities. This paper outlines a U-net architecture with novel preprocessing techniques, based on the windowing of training data and the modification of sigmoid activation threshold selection to successfully segment bone-bone marrow regions from low dose contrast enhanced whole-body CT scans. The proposed method achieved mean Dice coefficients of 0.979, 0.965, and 0.934 on two internal datasets and one external test dataset respectively. We have demonstrated that appropriate preprocessing is important for differentiating between bone and contrast dye, and that excellent results can be achieved with limited data.