Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge

2021-12-24 · Lucas Fidon, Suprosanna Shit, Ivan Ezhov, Johannes C. Paetzold, Sébastien Ourselin, Tom Vercauteren

Brain tumor segmentation from multiple Magnetic Resonance Imaging (MRI) modalities is a challenging task in medical image computation. The main challenges lie in the generalizability to a variety of scanners and imaging protocols. In this paper, we explore strategies to increase model robustness without increasing inference time. Towards this aim, we explore finding a robust ensemble from models trained using different losses, optimizers, and train-validation data split. Importantly, we explore the inclusion of a transformer in the bottleneck of the U-Net architecture. While we find transformer in the bottleneck performs slightly worse than the baseline U-Net in average, the generalized Wasserstein Dice loss consistently produces superior results. Further, we adopt an efficient test time augmentation strategy for faster and robust inference. Our final ensemble of seven 3D U-Nets with test-time augmentation produces an average dice score of 89.4% and an average Hausdorff 95% distance of 10.0 mm when evaluated on the BraTS 2021 testing dataset. Our code and trained models are publicly available at https://github.com/LucasFidon/TRABIT_BraTS2021.

📄 PDF Abstract BibTeX arXiv:2112.13054

Code (1)

lucasfidon/trabit_brats2021 공식 구현 pytorch

Tasks

Brain Tumor Segmentation Tumor Segmentation

Methods 이 논문이 사용한 방법론

Dice Loss \begin{equation} DiceLoss\left( y, \overline{p} \right) = 1 - \dfrac{\left( 2y\overline{p} + 1 \right)} {\left( y+\overline{p } + 1 \right)} \end{equation}

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