nnU-Net for Brain Tumor Segmentation
원문 초록
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.
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BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
Flexible Fusion Network for Multi-modal Brain Tumor Segmentation
PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning
BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective
토론
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