3D Deep Neural Network-Based Brain Tumor Segmentation Using Multimodality Magnetic Resonance Sequences

Brain tumor segmentation plays a pivotal role in clinical practice and research settings. In this paper, we propose a 3D deep neural network-based algorithm for joint brain tumor detection and intra-tumor structure segmentation, including necrosis, edema, non-enhancing and enhancing tumor, using multimodal magnetic resonance imaging sequences. An ensemble of cascaded U-Nets is designed to detect the tumor and a deep convolutional neural network is constructed for patch-based intra-tumor structure segmentation. This algorithm has been evaluated on the BraTS 2017 Challenge dataset and achieved Dice similarity coefficients of 0.81, 0.69 and 0.55 in the segmentation of whole tumor, core tumor and enhancing tumor, respectively. Our results suggest that the proposed algorithm has promising performance in automated brain tumor segmentation.

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

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

[3]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

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

[5]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[6]  Brian B. Avants,et al.  The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[7]  Yong Fan,et al.  Brain Tumor Segmentation Using a Fully Convolutional Neural Network with Conditional Random Fields , 2016, BrainLes@MICCAI.

[8]  Kebin Jia,et al.  Multiscale CNNs for Brain Tumor Segmentation and Diagnosis , 2016, Comput. Math. Methods Medicine.

[9]  S. McGuire.  World Cancer Report 2014. Geneva, Switzerland: World Health Organization, International Agency for Research on Cancer, WHO Press, 2015. , 2016, Advances in nutrition.

[10]  Hervé Delingette,et al.  A Patch-Based Approach for the Segmentation of Pathologies: Application to Glioma Labelling , 2016, IEEE Transactions on Medical Imaging.

[11]  Christos Davatzikos,et al.  GLISTR: Glioma Image Segmentation and Registration , 2012, IEEE Transactions on Medical Imaging.

[12]  Mark W. Schmidt,et al.  3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[13]  Koenraad Van Leemput,et al.  Brain Tumor Segmentation Using a Generative Model with an RBM Prior on Tumor Shape , 2015, Brainles@MICCAI.

[14]  Ben Glocker,et al.  Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR , 2012, MICCAI.

[15]  Stefan Bauer,et al.  Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization , 2011, MICCAI.

[16]  Michelle Chen,et al.  A Model for Spheroid versus Monolayer Response of SK-N-SH Neuroblastoma Cells to Treatment with 15-Deoxy-PGJ 2 , 2016, Comput. Math. Methods Medicine.

[17]  Victor Alves,et al.  On hierarchical brain tumor segmentation in MRI using fully convolutional neural networks: A preliminary study , 2017, 2017 IEEE 5th Portuguese Meeting on Bioengineering (ENBENG).

[18]  Brian B. Avants,et al.  Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR , 2014, Neuroinformatics.

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