  We present a fully automatic method employing convolutional neural networks
based on the 2D U-net architecture and random forest classifier to solve the
automatic liver lesion segmentation problem of the ISBI 2017 Liver Tumor
Segmentation Challenge (LiTS). In order to constrain the ROI in which the
tumors could be located, a liver segmentation is performed first. For the organ
segmentation, an ensemble of convolutional networks is trained to segment a
liver using a set of 179 liver CT datasets from liver surgery planning. Inside
of the liver ROI a neural network, trained using 127 challenge training
datasets, identifies tumor candidates, which are subsequently filtered with a
random forest classifier yielding the final tumor segmentation. The evaluation
on the 70 challenge test cases resulted in a mean Dice coefficient of 0.65,
ranking our method in the second place.
