Automatic Brain Tumor Segmentation Using Convolutional Neural Networks with Test-Time Augmentation
Authors:
- Guotai WangGWGuotai Wang
- Wenqi LiWLWenqi Li
- Sébastien OurselinSOSébastien Ourselin
- Tom VercauterenTVTom Vercauteren
- 26 January 2019
- book chapter
- Published by Springer Nature
- p. 61-72
Abstract
No abstract availableAll Related Versions
This publication has 21 references indexed in Scilit:
- Towards Safe Deep Learning: Accurately Quantifying Biomarker Uncertainty in Neural Network Predictions2018
- NiftyNet: a deep-learning platform for medical imagingComputer Methods and Programs in Biomedicine, 2018
- VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR imagesNeuroImage, 2018
- A deep 3D residualCNNfor false‐positive reduction in pulmonary nodule detectionMedical Physics, 2018
- Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation2018
- Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic featuresScientific Data, 2017
- Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentationMedical Image Analysis, 2017
- Brain tumor segmentation with Deep Neural NetworksMedical Image Analysis, 2017
- 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation2016
- Graph cut segmentation with a statistical shape model in cardiac MRIComputer Vision and Image Understanding, 2013