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Context Aware 3D CNNs for Brain Tumor Segmentation

Abstract : In this work we propose a novel deep learning based pipeline for the task of brain tumor segmentation. Our pipeline consists of three primary components: (i) a preprocessing stage that exploits histogram standardization to mitigate inaccuracies in measured brain modalities, (ii) a first prediction stage that uses the V-Net deep learning architecture to output dense, per voxel class probabilities, and (iii) a prediction refinement stage that uses a Conditional Random Field (CRF) with a bilateral filtering objective for better context awareness. Additionally, we compare the V-Net architecture with a custom 3D Residual Network architecture, trained on a multi-view strategy, and our ablation experiments indicate that V-Net outperforms the 3D ResNet-18 with all bells and whistles, while fully connected CRFs as post processing, boost the performance of both networks. We report competitive results on the BraTS 2018 validation and test set.
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Submitted on : Tuesday, December 18, 2018 - 7:04:49 PM
Last modification on : Saturday, January 15, 2022 - 4:12:07 AM
Long-term archiving on: : Wednesday, March 20, 2019 - 10:08:25 AM


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  • HAL Id : hal-01959610, version 1


Siddhartha Chandra, Maria Vakalopoulou, Lucas Fidon, Enzo Battistella, Théo Estienne, et al.. Context Aware 3D CNNs for Brain Tumor Segmentation. MICCAI Brainlesion Workshop, Sep 2018, Granada, Spain. ⟨hal-01959610⟩



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