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Context Aware 3D CNNs for Brain Tumor Segmentation

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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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Dates and versions

hal-01959610 , version 1 (18-12-2018)

Identifiers

  • HAL Id : hal-01959610 , version 1

Cite

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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