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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.
Cited literature [29 references]
https://hal.inria.fr/hal-01959610
Contributor : Maria Vakalopoulou <>
Submitted on : Tuesday, December 18, 2018 - 7:04:49 PM
Last modification on : Thursday, July 9, 2020 - 4:06:04 PM
Document(s) archivé(s) le : Wednesday, March 20, 2019 - 10:08:25 AM
Contributor : Maria Vakalopoulou <>
Submitted on : Tuesday, December 18, 2018 - 7:04:49 PM
Last modification on : Thursday, July 9, 2020 - 4:06:04 PM
Document(s) archivé(s) le : Wednesday, March 20, 2019 - 10:08:25 AM
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chandra_etal.pdf
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