Mimicking radiologists to improve the robustness of deep-learning based automatic liver segmentation

G. Chlebus, G. Humpire Mamani, A. Schenk, B. van Ginneken and H. Meine

Annual Meeting of the Radiological Society of North America 2019.

Radiologists delineating organ contours on a CT slice typically consider a couple of neighboring slices while taking into account the whole in-plane context in order to distinguish the organ boundary from surrounding structures. We present a new 3D deep-learning model that mimics the way radiologists interpret images on the example of liver segmentation. To evaluate its performance, the model is compared with a standard 3D neural network.