  Each year, there are about 400'000 new cases of kidney cancer worldwide
causing around 175'000 deaths. For clinical decision making it is important to
understand the morphometry of the tumor, which involves the time-consuming task
of delineating tumor and kidney in 3D CT images. Automatic segmentation could
be an important tool for clinicians and researchers to also study the
correlations between tumor morphometry and clinical outcomes. We present a
segmentation method which combines the popular U-Net convolutional neural
network architecture with post-processing based on statistical constraints of
the available training data. The full implementation, based on PyTorch, and the
trained weights can be found on GitHub.
