  Accurate segmentation of kidneys and kidney tumors is an essential step for
radiomic analysis as well as developing advanced surgical planning techniques.
In clinical analysis, the segmentation is currently performed by clinicians
from the visual inspection images gathered through a computed tomography (CT)
scan. This process is laborious and its success significantly depends on
previous experience. Moreover, the uncertainty in the tumor location and
heterogeneity of scans across patients increases the error rate. To tackle this
issue, computer-aided segmentation based on deep learning techniques have
become increasingly popular. We present a multi-scale supervised 3D U-Net, MSS
U-Net, to automatically segment kidneys and kidney tumors from CT images. Our
architecture combines deep supervision with exponential logarithmic loss to
increase the 3D U-Net training efficiency. Furthermore, we introduce a
connected-component based post processing method to enhance the performance of
the overall process. This architecture shows superior performance compared to
state-of-the-art works using data from KiTS19 public dataset, with the Dice
coefficient of kidney and tumor up to 0.969 and 0.805 respectively. The
segmentation techniques introduced in this paper have been tested in the KiTS19
challenge with its corresponding dataset.
