3D MRI brain tumor segmentation using autoencoder regularization

Published in

(2018)

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TL;DR

Due to a limited training dataset size, a variational auto-encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers.

Abstract

Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease. Manual delineation practices require anatomical knowledge, are expensive, time consuming and can be inaccurate due to human error. Here, we describe a semantic segmentation network for tumor subregion segmentation from 3D MRIs based on encoder-decoder architecture. Due to a limited training dataset size, a variational auto-encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers. The current approach won 1st place in the BraTS 2018 challenge.

Authors

Andriy Myronenko

2 papers

References23 items

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Learning Contextual and Attentive Information for Brain Tumor Segmentation

Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation

No New-Net

Group Normalization

3D MRI brain tumor segmentation using autoencoder regularization

Published in

(2018)

External Links:

Generate Graph

TL;DR

Abstract

Authors

Andriy Myronenko

2 papers

References23 items

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Learning Contextual and Attentive Information for Brain Tumor Segmentation

Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation

No New-Net

Group Normalization

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks

Xception: Deep Learning with Depthwise Separable Convolutions

Densely Connected Convolutional Networks

Tutorial on Variational Autoencoders

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

Identity Mappings in Deep Residual Networks

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

U-Net: Convolutional Networks for Biomedical Image Segmentation

Fully convolutional networks for semantic segmentation

Auto-Encoding Variational Bayes

Segmentation labels and radiomic features for the pre-operative scans of the tcga-lgg collection