In this paper we propose a fully automatic 2-stage cascaded approach for
segmentation of liver and its tumors in CT (Computed Tomography) images using
densely connected fully convolutional neural network (DenseNet). We
independently train liver and tumor segmentation models and cascade them for a
combined segmentation of the liver and its tumor...
The first stage involves
segmentation of liver and the second stage uses the first stage's segmentation
results for localization of liver and henceforth tumor segmentations inside
liver region. The liver model was trained on the down-sampled axial slices
$(256 \times 256)$, whereas for the tumor model no down-sampling of slices was
done, but instead it was trained on the CT axial slices windowed at three
different Hounsfield (HU) levels. On the test set our model achieved a global
dice score of 0.923 and 0.625 on liver and tumor respectively. The computed
tumor burden had an rmse of 0.044.