  Automatic segmentation of the liver and its lesion 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 abdomen images using cascaded
fully convolutional neural networks (CFCNs) and dense 3D conditional random
fields (CRFs). 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 from the
predicted liver ROIs of step 1. We refine the segmentations of the CFCN using a
dense 3D CRF that accounts for both spatial coherence and appearance. CFCN
models were trained in a 2-fold cross-validation on the abdominal CT dataset
3DIRCAD comprising 15 hepatic tumor volumes. Our results show that CFCN-based
semantic liver and lesion segmentation achieves Dice scores over 94% for liver
with computation times below 100s per volume. We experimentally demonstrate the
robustness of the proposed method as a decision support system with a high
accuracy and speed for usage in daily clinical routine.
