Automatic Couinaud Segmentation from CT Volumes on Liver Using GLC-UNet

Abstract

Automatically generating Couinaud segments on liver, a prerequisite for modern surgery of the liver, from computed tomography (CT) volumes is a challenge for the computer-aided diagnosis (CAD). In this paper, we propose a novel global and local contexts UNet (GLC-UNet) for Couinaud segmentation. In this framework, intra-slice features and 3D contexts are effectively probed and jointly optimized for accurate liver and Couinaud segmentation using attention mechanism. We comprehensively evaluate our system performance ($$98.51\%$$ in terms of Dice per case on liver segmentation, and $$92.46\%$$ on Couinaud segmentation) on the Medical Segmentation Decathlon dataset (task 8, hepatic vessels and tumor) from MICCAI 2018 with our annotated 43, 205 CT slices on liver and Couinaud segmentation. (https://github.com/GLCUnet/dataset ).