nnU-Net for Brain Tumor Segmentation (2020-11-02T00:00:00.000000Z)

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

TL;DR

Abstract

Authors

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

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

No New-Net

3D MRI brain tumor segmentation using autoencoder regularization

Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation

No New-Net

Why rankings of biomedical image analysis competitions should be interpreted with care

Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks

Focal Loss for Dense Object Detection

Attention is All you Need

Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response

Densely Connected Convolutional Networks

The Importance of Skip Connections in Biomedical Image Segmentation

Instance Normalization: The Missing Ingredient for Fast Stylization

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Deep Residual Learning for Image Recognition

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

U-Net: Convolutional Networks for Biomedical Image Segmentation

I and i

A and V

Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II

Segmentation labels and radiomic features for the pre-operative scans of the tcga-lgg collection

Rectifier Nonlinearities Improve Neural Network Acoustic Models

I and J

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

nnU-Net for Brain Tumor

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