  Automatic segmentation of liver and its tumors is an essential step for
extracting quantitative imaging biomarkers for accurate tumor detection,
diagnosis, prognosis and assessment of tumor response to treatment. MICCAI 2017
Liver Tumor Segmentation Challenge (LiTS) provides a common platform for
comparing different automatic algorithms on contrast-enhanced abdominal CT
images in tasks including 1) liver segmentation, 2) liver tumor segmentation,
and 3) tumor burden estimation. We participate this challenge by developing a
hierarchical framework based on deep fully convolutional-deconvolutional neural
networks (CDNN). A simple CDNN model is firstly trained to provide a quick but
coarse segmentation of the liver on the entire CT volume, then another CDNN is
applied to the liver region for fine liver segmentation. At last, the segmented
liver region, which is enhanced by histogram equalization, is employed as an
additional input to the third CDNN for tumor segmentation. Jaccard distance is
used as loss function when training CDNN models to eliminate the need of sample
re-weighting. Our framework is trained using the 130 challenge training cases
provided by LiTS. The evaluation on the 70 challenge testing cases resulted in
a mean Dice Similarity Coefficient (DSC) of 0.963 for liver segmentation, a
mean DSC of 0.657 for tumor segmentation, and a root mean square error (RMSE)
of 0.017 for tumor burden estimation, which ranked our method in the first,
fifth and third place, respectively
