  Automatic segmentation of the liver and hepatic lesions is an important step
towards deriving quantitative biomarkers for accurate clinical diagnosis and
computer-aided decision support systems. This paper presents a method to
automatically segment liver and lesions in CT and MRI abdomen images using
cascaded fully convolutional neural networks (CFCNs) enabling the segmentation
of a large-scale medical trial or quantitative image analysis. We train and
cascade two FCNs for a combined segmentation of the liver and its lesions. In
the first step, we train a FCN to segment the liver as ROI input for a second
FCN. The second FCN solely segments lesions within the predicted liver ROIs of
step 1. CFCN models were trained on an abdominal CT dataset comprising 100
hepatic tumor volumes. Validations on further datasets show that CFCN-based
semantic liver and lesion segmentation achieves Dice scores over 94% for liver
with computation times below 100s per volume. We further experimentally
demonstrate the robustness of the proposed method on an 38 MRI liver tumor
volumes and the public 3DIRCAD dataset.
