Bone Segmentation in Contrast Enhanced Whole-Body Computed Tomography
This work addresses a domain-specific challenge in medical imaging for enhanced diagnostics and treatment monitoring, but it is incremental as it builds on existing U-net methods with preprocessing improvements.
The paper tackled the problem of accurate automatic bone segmentation in low-dose contrast-enhanced whole-body CT scans, where image quality is reduced, by proposing a U-net architecture with novel preprocessing techniques, achieving mean Dice coefficients of 0.979, 0.965, and 0.934 on internal and external datasets.
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.