Applying nnU-Net to the KiTS19 Grand Challenge
John Brandon Graham-Knight, Abtin Djavadifar, Patricia Lasserre, Homayoun Najjaran · 2019
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.