Research on glioma magnetic resonance imaging segmentation based on dual-channel three-dimensional densely connected network_Journal of Biomedical Engineering

Authors：

 HUO Zhiyong ¹ , DU Shuaiyu ² , CHEN Zhao ² , DAI Weida ²

1. Jiangsu Province Key Lab on Image Processing & Image Communication, Nanjing University of Posts and Telecommunications, Nanjing 210003, P.R.China;
2. College of Telecommunication & Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, P.R.China;

Corresponding author：

HUO Zhiyong, Email: huozy@njupt.edu.cn

Keywords：

three-dimensional convolutional neural network; densely connected block; magnetic resonance imaging; brain glioma segmentation

DOI：

10.7507/1001-5515.201902006

Video：

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Abstract Full text Figures/Tables Video References Cited by

Focus on the inconsistency of the shape, location and size of brain glioma, a dual-channel 3-dimensional (3D) densely connected network is proposed to automatically segment brain glioma tumor on magnetic resonance images. Our method is based on a 3D convolutional neural network frame, and two convolution kernel sizes are adopted in each channel to extract multi-scale features in different scales of receptive fields. Then we construct two densely connected blocks in each pathway for feature learning and transmission. Finally, the concatenation of two pathway features was sent to classification layer to classify central region voxels to segment brain tumor automatically. We train and test our model on open brain tumor segmentation challenge dataset, and we also compared our results with other models. Experimental results show that our algorithm can segment different tumor lesions more accurately. It has important application value in the clinical diagnosis and treatment of brain tumor diseases.

Citation： HUO Zhiyong, DU Shuaiyu, CHEN Zhao, DAI Weida. Research on glioma magnetic resonance imaging segmentation based on dual-channel three-dimensional densely connected network. Journal of Biomedical Engineering, 2019, 36(5): 763-768, 776. doi: 10.7507/1001-5515.201902006 Copy

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1. Shanthi K J, Kumar M S. Skull stripping and automatic segmentation of brain MRI using seed growth and threshold techniques//2007 International Conference on Intelligent and Advanced Systems (ICIAS 2007). IEEE, 2007: 422-426.
2. Kaleem M, Sanaullah M, Ayyaz Hussain M, et al. Segmentation of brain tumor tissue using marker controlled watershed transform method//International Multi Topic Conference(IMTIC 2012). Springer, 2012: 222-227.
3. Kaus M R, Warfield S K, Nabavi A, et al. Automated segmentation of MR images of brain tumors. Radiology, 2001, 218(2): 586-591.
4. Lakare S. 3D segmentation techniques for medical volumes. New York: State University of New York, 2000.
5. Zikic D, Ioannou Y, Brown M, et al. Segmentation of brain tumor Tissues with convolutional neural networks//MICCAI workshop on Multimodal Brain Tumor Segmentation Challenge (BRATS), Springer, 2014: 36-39.
6. Chang P D. Fully convolutional neural networks with hyperlocal features for brain tumor segmentation//Proceedings MICCAI-BRATS Workshop, Springer, 2016: 4-9.
7. Zhao Liya, Jia Kebin. Multiscale CNNs for brain tumor segmentation and diagnosis. Comput Math Methods Med, 2016, 2016: 8356294.
8. Kamnitsas K, Ledig C, Newcombe V F, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal, 2017, 36: 61-78.
9. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation//Medical Image Computing and Computer-Assisted Intervention(MICCAI), Springer, 2015, 9351: 234-241.
10. Huang Gao, Liu Zhuang, van Der Maaten L, et al. Densely connected convolutional networks//30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017: 2261-2269.
11. Menze B H, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS) IEEE Trans Med Imaging, 2015, 34(10): 1993-2024.
12. Tustison N J, Gee J C. N4ITK: Nick's N3 ITK implementation for MRI bias field correction. OR Insight, 2009: 1-8.
13. Bharath H N, Colleman S, Sima D M, et al. Tumor segmentation from multimodal MRI using random forest with superpixel and tensor based feature extraction. BrainLes 2017: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 2017: 463-473.
14. Chen X, Nguyen B P, Chui C K, et al. Reworking multilabel brain tumor segmentation: an automated framework using structured kernel sparse representation. IEEE Systems, Man, and Cybernetics Magazine, 2017, 3(2): 18-22.
15. Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal, 2017, 35: 18-31.
16. Pereira S, Pinto A, Alves V, et al. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging, 2016, 35(5): 1240-1251.

Journal of Biomedical Engineering

Research on glioma magnetic resonance imaging segmentation based on dual-channel three-dimensional densely connected network

Abstract Full text Figures/Tables Video References Cited by

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