Applying nnU-Net to the KiTS19 Grand Challenge

Authors
Graham-Knight, J. B.
Djavadifar, A.
Lasserre, P.
Najjaran, H.

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
U-Net, conceived in 2015, is making a resurgence in medical semantic segmentation tasks. This comeback is largely thanks to the excellent performance of nnU-Net in recent competitions. nnU-Net generalizes well, as proven by its first-place finish in the Medical Segmentation Decathalon. Notably, nnU-Net focuses on the training process rather than algorithmic improvements, and can often beat more complex algorithms. This paper shows the results of applying nnU-Net to the KiTS19 Kidney Segmentation Grand Challenge. Each of the 5 cross-validation training folds achieves good results, with scores nearing or exceeding 0.9 after approximately 500 epochs per fold.

Identifiers
doi: 10.24926/548719.015