Spectral CT Based Training Dataset Generation and Augmentation for Conventional CT Vascular Segmentation

Abstract

Deep learning has proved to be a very efficient tool for organs automated segmentation in CT scans. However, variation of iodine contrast agent concentration within the vascular system or organs is a major source of variation in image contrast. This requires building large databases representative of the important differences in contrast enhancement across CT studies. Furthermore, creating a low- or non-enhanced annotated database is still a very laborious task as semi-automatic segmentation software and even expert eyes often fail to find structures’ edges on low contrast images.