  Automatic liver lesion segmentation is a challenging task while having a
significant impact on assisting medical professionals in the designing of
effective treatment and planning proper care. In this paper we propose a
cascaded system that combines both 2D and 3D convolutional neural networks to
effectively segment hepatic lesions. Our 2D network operates on a slice by
slice basis to segment the liver and larger tumors, while we use a 3D network
to detect small lesions that are often missed in a 2D segmentation design. We
employ this algorithm on the LiTS challenge obtaining a Dice score per case of
68.1%, which performs the best among all non pre-trained models and the second
best among published methods. We also perform two-fold cross-validation to
reveal the over- and under-segmentation issues in the LiTS annotations.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning