  Automatic segmentation of hepatic lesions in computed tomography (CT) images
is a challenging task to perform due to heterogeneous, diffusive shape of
tumors and complex background. To address the problem more and more researchers
rely on assistance of deep convolutional neural networks (CNN) with 2D or 3D
type architecture that have proven to be effective in a wide range of computer
vision tasks, including medical image processing. In this technical report, we
carry out research focused on more careful approach to the process of learning
rather than on complex architecture of the CNN. We have chosen MICCAI 2017 LiTS
dataset for training process and the public 3DIRCADb dataset for validation of
our method. The proposed algorithm reached DICE score 78.8% on the 3DIRCADb
dataset. The described method was then applied to the 2019 Kidney Tumor
Segmentation (KiTS-2019) challenge, where our single submission achieved 96.38%
for kidney and 67.38% for tumor Dice scores.
