Liver lesion segmentation informed by joint liver segmentation

Understand

We propose a model for the joint segmentation of the liver and liver lesions in computed tomography (CT) volumes.

• We build the model from two fully convolutional networks, connected in tandem and trained together end-to-end.
• We evaluate our approach on the 2017 MICCAI Liver Tumour Segmentation Challenge, attaining competitive liver and liver lesion detection and segmentation scores across a wide range of metrics.
• Unlike other top performing methods, our model output post-processing is trivial, we do not use data external to the challenge, and we propose a simple single-stage model that is trained end-to-end.