  U-Net has achieved huge success in various medical image segmentation
challenges. Kinds of new architectures with bells and whistles might succeed in
certain dataset when employed with optimal hyper-parameter, but their
generalization always can't be guaranteed. Here, we focused on the basic U-Net
architecture and proposed a multi scale supervised 3D U-Net for the
segmentation task in KiTS19 challenge. To enhance the performance, our work can
be summarized as three folds: first, we used multi scale supervision in the
decoder pathway, which could encourage the network to predict right results
from the deep layers; second, with the aim to alleviate the bad effect from the
sample imbalance of kidney and tumor, we adopted exponential logarithmic loss;
third, a connected-component based post processing method was designed to
remove the obviously wrong voxels. In the published KiTS19 training dataset
(totally 210 patients), we divided 42 patients to be test dataset and finally
obtained DICE scores of 0.969 and 0.805 for the kidney and tumor respectively.
In the challenge, we finally achieved the 7th place among 106 teams with the
Composite Dice of 0.8961, namely 0.9741 for kidney and 0.8181 for tumor.
