nnU-Net for Brain Tumor Segmentation

Published in

(2020)

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TL;DR

By incorporating BraTS-specific modifications regarding postprocessing, region-based training, a more aggressive data augmentation as well as several minor modifications to the nnUNet pipeline, nnU-Net is able to improve its segmentation performance substantially.

Abstract

We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified nnU-Net baseline configuration already achieves a respectable result. By incorporating BraTS-specific modifications regarding postprocessing, region-based training, a more aggressive data augmentation as well as several minor modifications to the nnUNet pipeline we are able to improve its segmentation performance substantially. We furthermore re-implement the BraTS ranking scheme to determine which of our nnU-Net variants best fits the requirements imposed by it. Our final ensemble took the first place in the BraTS 2020 competition with Dice scores of 88.95, 85.06 and 82.03 and HD95 values of 8.498,17.337 and 17.805 for whole tumor, tumor core and enhancing tumor, respectively.

Authors

Fabian Isensee

6 papers

P. Jaeger

3 papers

Klaus Hermann Maier-Hein

7 papers

References36 items

Bag of Tricks for 3D MRI Brain Tumor Segmentation

Two-Stage Cascaded U-Net: 1st Place Solution to BraTS Challenge 2019 Segmentation Task

Triplanar Ensemble of 3D-to-2D CNNs with Label-Uncertainty for Brain Tumor Segmentation

Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

nnU-Net for Brain Tumor Segmentation

Published in

(2020)

External Links:

Generate Graph

TL;DR

Abstract

Authors

Fabian Isensee

6 papers

P. Jaeger

3 papers

Klaus Hermann Maier-Hein

7 papers

References36 items

Bag of Tricks for 3D MRI Brain Tumor Segmentation

Two-Stage Cascaded U-Net: 1st Place Solution to BraTS Challenge 2019 Segmentation Task

Triplanar Ensemble of 3D-to-2D CNNs with Label-Uncertainty for Brain Tumor Segmentation

Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Peter M. Full

2 papers

Philipp Vollmuth

1 papers

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

3D MRI brain tumor segmentation using autoencoder regularization

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Rectifier Nonlinearities Improve Neural Network Acoustic Models

Influence of cultivation temperature on the ligninolytic activity of selected fungal strains