CANet: Context Aware Network for 3D Brain Tumor Segmentation

Automated segmentation of brain tumors in 3D magnetic resonance imaging plays an active role in tumor diagnosis, progression monitoring and surgery planning. Based on convolutional neural networks, especially fully convolutional networks, previous studies have shown some promising technologies for brain tumor segmentation. However, these approaches lack suitable strategies to incorporate contextual information to deal with local ambiguities, leading to unsatisfactory segmentation outcomes in challenging circumstances. In this work, we propose a novel Context-Aware Network (CANet) with a Hybrid Context Aware Feature Extractor (HCA-FE) and a Context Guided Attentive Conditional Random Field (CG-ACRF) for feature fusion. HCA-FE captures high dimensional and discriminative features with the contexts from both the convolutional space and feature interaction graphs. We adopt the powerful inference ability of probabilistic graphical models to learn hidden feature maps, and then use CG-ACRF to fuse the features of different contexts. We evaluate our proposed method on publicly accessible brain tumor segmentation datasets BRATS2017 and BRATS2018 against several state-of-the-art approaches using different segmentation metrics. The experimental results show that the proposed algorithm has better or competitive performance, compared to the standard approaches.

[1]  Hongliang Ren,et al.  Multi-modal PixelNet for Brain Tumor Segmentation , 2017, BrainLes@MICCAI.

[2]  Dorit Merhof,et al.  Segmentation of Brain Tumors and Patient Survival Prediction: Methods for the BraTS 2018 Challenge , 2018, BrainLes@MICCAI.

[3]  Andriy Myronenko,et al.  3D MRI brain tumor segmentation using autoencoder regularization , 2018, BrainLes@MICCAI.

[4]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[5]  Hoo-Chang Shin,et al.  Hybrid Clustering and Logistic Regression for Multi-Modal Brain Tumor Segmentation , 2012 .

[6]  Klaus H. Maier-Hein,et al.  No New-Net , 2018, 1809.10483.

[7]  J. Gregory Pauloski,et al.  Glioma Segmentation and a Simple Accurate Model for Overall Survival Prediction , 2018, BrainLes@MICCAI.

[8]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[9]  Christos Davatzikos,et al.  Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features , 2017, Scientific Data.

[10]  Victor Alves,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2016, IEEE Transactions on Medical Imaging.

[11]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[12]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[13]  Richard McKinley,et al.  Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation , 2018, BrainLes@MICCAI.

[14]  Tal Arbel,et al.  Brain Tumor Segmentation Using a 3D FCN with Multi-scale Loss , 2017, BrainLes@MICCAI.

[15]  Loïc Le Folgoc,et al.  Attention U-Net: Learning Where to Look for the Pancreas , 2018, ArXiv.

[16]  Victor Alves,et al.  Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks , 2019, IEEE Transactions on Medical Imaging.

[17]  Vladlen Koltun,et al.  Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.

[18]  Li Sun,et al.  Tumor Segmentation and Survival Prediction in Glioma with Deep Learning , 2018, BrainLes@MICCAI.

[19]  T. Arbel,et al.  Probabilistic Gabor and Markov Random Fields Segmentation of B rain Tumours in MRI Volumes , 2012 .

[20]  Xavier Lladó,et al.  Survival prediction using ensemble tumor segmentation and transfer learning , 2018, ArXiv.

[21]  Alan L. Yuille,et al.  Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification , 2008, IEEE Transactions on Medical Imaging.

[22]  Sébastien Ourselin,et al.  Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks , 2017, BrainLes@MICCAI.

[23]  Nicholas J. Tustison,et al.  Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features , 2018, Frontiers in Computational Neuroscience.

[24]  Víctor M. Pérez-García,et al.  Towards Uncertainty-Assisted Brain Tumor Segmentation and Survival Prediction , 2017, BrainLes@MICCAI.

[25]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Rasmus Larsen,et al.  An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation , 2015, SCIA.

[27]  Yong Fan,et al.  3D Brain Tumor Segmentation Through Integrating Multiple 2D FCNNs , 2017, BrainLes@MICCAI.

[28]  Dacheng Tao,et al.  Learning Contextual and Attentive Information for Brain Tumor Segmentation , 2018, BrainLes@MICCAI.

[29]  Ender Konukoglu,et al.  A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation , 2019, MICCAI.

[30]  Konstantinos Kamnitsas,et al.  Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation , 2017, BrainLes@MICCAI.

[31]  Nassir Navab,et al.  Recalibrating Fully Convolutional Networks With Spatial and Channel “Squeeze and Excitation” Blocks , 2018, IEEE Transactions on Medical Imaging.

[32]  Sim Heng Ong,et al.  Focus, Segment and Erase: An Efficient Network for Multi-label Brain Tumor Segmentation , 2018, ECCV.

[33]  Antonio Criminisi,et al.  Segmentation of Brain Tumor Tissues with Convolutional Neural Networks , 2014 .

[34]  Sachin Mehta,et al.  3D-ESPNet with Pyramidal Refinement for Volumetric Brain Tumor Image Segmentation , 2018, BrainLes@MICCAI.

[35]  Gustavo Carneiro,et al.  A Discriminative Model-Constrained Graph Cuts Approach to Fully Automated Pediatric Brain Tumor Segmentation in 3-D MRI , 2008, MICCAI.

[36]  Klaus H. Maier-Hein,et al.  Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge , 2017, BrainLes@MICCAI.

[37]  Guang Yang,et al.  Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks , 2017, MIUA.

[38]  Yong Fan,et al.  A deep learning model integrating FCNNs and CRFs for brain tumor segmentation , 2017, Medical Image Anal..