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

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

in: 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.