Segmentation of bones in medical dual-energy computed tomography volumes using the 3D U-Net. (January 2020) - González Sánchez, José Carlos
Highlights: The proposed 3D U-Net could be used for the segmentation of pelvic bones in dual-energy computed tomography. Better segmentation results were achieved with dual-energy CT images than mixed images. Differences in segmentation results between evaluated models were mostly at bone edges. Abstract: Deep learning algorithms have improved the speed and quality of segmentation for certain tasks in medical imaging. The aim of this work is to design and evaluate an algorithm capable of segmenting bones in dual-energy CT data sets. A convolutional neural network based on the 3D U-Net architecture was implemented and evaluated using high tube voltage images, mixed images and dual-energy images from 30 patients. The network performed well on all the data sets; the mean Dice coefficient for the test data was larger than 0.963. Of special interest is that it performed better on dual-energy CT volumes compared to mixed images that mimicked images taken at 120 kV. The corresponding increase in the Dice coefficient from 0.965 to 0.966 was small since the enhancements were mainly at the edges of the bones. The method can easily be extended to the segmentation of multi-energy CT data.