Optimized U-Net for Brain Tumor Segmentation

2021/10/07 by Futrega, Michał, Milesi, Alexandre, Marcinkiewicz, Michal +1 · 3 citations
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2110.03352

Abstract

We propose an optimized U-Net architecture for a brain tumor segmentation task in the BraTS21 challenge. To find the optimal model architecture and the learning schedule, we have run an extensive ablation study to test: deep supervision loss, Focal loss, decoder attention, drop block, and residual connections. Additionally, we have searched for the optimal depth of the U-Net encoder, number of convolutional channels and post-processing strategy. Our method won the validation phase and took third place in the test phase. We have open-sourced the code to reproduce our BraTS21 submission at the NVIDIA Deep Learning Examples GitHub Repository.

Cited by

• ReCoSeg++:Extended Residual-Guided Cross-Modal Diffusion for Brain Tumor Segmentation
• Multimodal Fusion at Three Tiers: Physics-Driven Data Generation and Vision-Language Guidance for Brain Tumor Segmentation
• SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation

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• Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net\n neural networks: a BraTS 2020 challenge solution
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