Brain Tumor Segmentation Using Deep Fully Convolutional Neural Networks

In this study, brain tumor substructures are segmented using 2D fully convolutional neural networks. A number of modifications such as double convolution layers, inception modules, and dense modules were added to a U-Net to achieve a deep architecture and test if the increased depth improves the performance. The experiments show that the deep architectures improve the performance. Also, the performance is enhanced from ensembling across the models trained on images in different orientations and ensembling across the models with different architectures. Even without any data augmentation, the ensembled model achieves a competitive performance and generalizes well on a new dataset. The resulting mean 3D Dice scores (ET/WT/TC) on the BRATS17 validation and test sets are 0.75/0.88/0.73 and 0.72/0.86/0.73.

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

[2]  Heinz Handels,et al.  Image Features for Brain Lesion Segmentation Using Random Forests , 2015, Brainles@MICCAI.

[3]  Mauricio Reyes,et al.  Parameter Learning for CRF-Based Tissue Segmentation of Brain Tumors , 2015, Brainles@MICCAI.

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

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

[6]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[7]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[8]  Christopher Joseph Pal,et al.  A Convolutional Neural Network Approach to Brain Tumor Segmentation , 2015, Brainles@MICCAI.

[9]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Xiaojing Chen,et al.  Anatomy-Guided Brain Tumor Segmentation and Classification , 2016, BrainLes@MICCAI.

[11]  Victor Alves,et al.  Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRI , 2015, Brainles@MICCAI.

[12]  Konstantinos Kamnitsas,et al.  DeepMedic for Brain Tumor Segmentation , 2016, BrainLes@MICCAI.

[13]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Christos Davatzikos,et al.  Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework , 2016, BrainLes@MICCAI.

[15]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

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

[17]  Peter D. Chang,et al.  Fully Convolutional Deep Residual Neural Networks for Brain Tumor Segmentation , 2016, BrainLes@MICCAI.

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

[19]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[21]  Bilwaj Gaonkar,et al.  GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation , 2015, Brainles@MICCAI.

[22]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

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

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