Research on brain image segmentation based on deep learning_Journal of Biomedical Engineering

Authors：

WANG Yuli ,  ZHAO Zijian

School of Control Science and Engineering, Shandong University, Jinan 250061, P.R.China;

Corresponding author：

Keywords：

deep learning; convolutional neural network; brain image segmentation; prior knowledge; general deep learning models

DOI：

10.7507/1001-5515.201912050

Video：

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Brain image segmentation algorithm based on deep learning is a research hotspot at present. In this paper, firstly, the significance of brain image segmentation and the content of related brain image segmentation algorithm are systematically described, highlighting the advantages of brain image segmentation algorithms based on deep learning. Then, this paper introduces current brain image segmentation algorithms based on deep learning from three aspects: the brain image segmentation algorithms based on problems existent to brain image, the brain image segmentation algorithms based on prior knowledge guidance and the application of general deep learning models in brain image segmentation, so as to enable researchers in relevant fields to understand current research progress more systematically. Finally, this paper provides a general direction for the further research of brain image segmentation algorithm based on deep learning.

Citation： WANG Yuli, ZHAO Zijian. Research on brain image segmentation based on deep learning. Journal of Biomedical Engineering, 2020, 37(4): 721-729, 735. doi: 10.7507/1001-5515.201912050 Copy

1.	Shah N, Gravel E, Jawale D V, et al. ChemInform abstract:synthesis of quinoxalines by a carbon nanotube-gold nanohybrid-catalyzed cascade reaction of vicinal diols and Keto alcohols with diamines. ChemCatChem, 2015, 7(1): 57-61.
2.	冷月爽, 王小宜, 廖伟华, 等. 影像组学在胶质瘤临床研究中的应用进展. 中南大学学报:医学版, 2018, 43(4): 354-359.
3.	Isensee F, Kickingereder P, Wick W, et al. Brain tumor segmentation and radiomics survival prediction: contribution to the brats 2017 challenge// International MICCAI Brainlesion Workshop. Cham: Springer, 2017: 287-297.
4.	Menze B H, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging, 2014, 34(10): 1993-2024.
5.	Wu G, Wang Q, Zhang D, et al. A generative probability model of joint label fusion for multi-atlas based brain segmentation. Med Image Anal, 2014, 18(6): 881-890.
6.	Campadelli P, Casiraghi E, Esposito A. Liver segmentation from computed tomography scans: a survey and a new algorithm. Artif Intell Med, 2009, 45(2-3): 185-196.
7.	Işın A, Direkoğlu C, Şah M. Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Computer Science, 2016, 102: 317-324.
8.	Grosu A L, Weber W A. PET for radiation treatment planning of brain tumours. Radiotherapy & Oncology, 2010, 96(3): 325-327.
9.	Ibtehaz N, Rahman M S. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks, 2020, 121: 74-87.
10.	Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Medical Image Analysis, 2017, 35: 18-31.
11.	Myronenko A. 3D MRI brain tumor segmentation using autoencoder regularization// International MICCAI Brainlesion Workshop. Cham: Springer, 2018: 311-320.
12.	Dalca A V, Guttag J, Sabuncu M R. Anatomical priors in convolutional networks for unsupervised biomedical segmentation//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 9290-9299.
13.	Chen Liang, Bentley P, Rueckert D. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. Neuroimage: Clinical, 2017, 15: 633-643.
14.	Hussain S, Anwar S M, Majid M. Segmentation of glioma tumors in brain using deep convolutional neural network. Neurocomputing, 2018, 282: 248-261.
15.	Brügger R, Baumgartner C F, Konukoglu E. A partially reversible U-Net for memory-efficient volumetric image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention, Cham: Springer, 2019: 429-437.
16.	Wang G, Li W, Ourselin S, et al. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks//International Conference on Medical Image Computing and Computer-Assisted Intervention, Cham: Springer, 2017: 178-190.
17.	Mlynarski P, Delingette H, Criminisi A, et al. 3D convolutional neural networks for tumor segmentation using long-range 2D context. Computerized Medical Imaging and Graphics, 2019, 73: 60-72.
18.	Wang G, Li W, Zuluaga M A, et al. Interactive medical image segmentation using deep learning with image-specific fine-tuning. IEEE Trans Med Imaging, 2018, 37(7): 1562-1573.
19.	Zhou Chenhong, Chen Shengcong, Ding Changxing, et al. Learning contextual and attentive information for brain tumor segmentation// International MICCAI Brainlesion Workshop. Cham: Springer, 2018: 497-507.
20.	Kao P Y, Chen J W, Manjunath B S. Improving 3D U-Net for brain tumor segmentation by utilizing lesion prior. Computer Vision and Pattern Recognition, 2019. arXiv: 1907.00281.
21.	Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention, Cham: Springer, 2015: 234-241.
22.	Kayalibay B, Jensen G, van der Smagt P. CNN-based segmentation of medical imaging data. Computer Vision and Pattern Recognition, 2017. arXiv: 1701.03056.
23.	Mckinley R, Meier R, Wiest R. Ensembles of densely-connected cnns with label-uncertainty for brain tumor segmentation// International MICCAI Brainlesion Workshop. Cham: Springer, 2018: 456-465.
24.	Isensee F, Kickingereder P, Wick W, et al. No New-Net// International MICCAI Brainlesion Workshop. Cham: Springer, 2018: 234-244.
25.	Liu Y, Stojadinovic S, Hrycushko B, et al. A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery. PLoS One, 2017, 12(10): e0185844.
26.	Chen H, Dou Q, Yu L, et al. VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage, 2018, 170: 446-455.
27.	Kamnitsas K, Bai Wenjia, Ferrante E, et al. Ensembles of multiple models and architectures for robust brain tumour segmentation// International MICCAI Brainlesion Workshop. Cham: Springer, 2017: 450-462.
28.	Iqbal S, Khan M G, Saba T, et al. Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation. Microsc Res Tech, 2019, 82(8): 1302-1315.
29.	Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 39(4): 640-651.
30.	Kamnitsas K, Ledig C, Newcombe V F J, et al. Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical Image Analysis, 2017, 36: 61-78.
31.	Zhao Xiaomei, Wu Yihong, Song Guidong, et al. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Medical Image Analysis, 2018, 43: 98-111.
32.	Xue Y, Xu T, Zhang H, et al. SegAN: adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics, 2018, 16(3-4): 383-392.
33.	Soltaninejad M, Zhang Lei, Lambrou T, et al. Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network. Computer Vision and Pattern Recognition, 2017. arXiv: 1704.08134.
34.	Li T, Zhou F, Zhu Z, et al. A label-fusion-aided convolutional neural network for isointense infant brain tissue segmentation//IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018: 692-695.
35.	Sharma M, Purohit G N, Mukherjee S. Information retrieves from brain MRI images for tumor detection using hybrid technique K-means and artificial neural network (KMANN)// Networking Communication and Data Knowledge Engineering. Singapore: Springer, 2018: 145-157.
36.	Ahmmed R, Swakshar A S, Hossain M F, et al. Classification of tumors and it stages in brain MRI using support vector machine and artificial neural network// 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE). Cox's Bazar: IEEE, 2017: 229-234.
37.	Mohsen H, El-Dahshan E A, El-Horbaty E M, et al. Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 2018, 3(1): 68-71.

1. Shah N, Gravel E, Jawale D V, et al. ChemInform abstract:synthesis of quinoxalines by a carbon nanotube-gold nanohybrid-catalyzed cascade reaction of vicinal diols and Keto alcohols with diamines. ChemCatChem, 2015, 7(1): 57-61.
2. 冷月爽, 王小宜, 廖伟华, 等. 影像组学在胶质瘤临床研究中的应用进展. 中南大学学报:医学版, 2018, 43(4): 354-359.
3. Isensee F, Kickingereder P, Wick W, et al. Brain tumor segmentation and radiomics survival prediction: contribution to the brats 2017 challenge// International MICCAI Brainlesion Workshop. Cham: Springer, 2017: 287-297.
4. Menze B H, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging, 2014, 34(10): 1993-2024.
5. Wu G, Wang Q, Zhang D, et al. A generative probability model of joint label fusion for multi-atlas based brain segmentation. Med Image Anal, 2014, 18(6): 881-890.
6. Campadelli P, Casiraghi E, Esposito A. Liver segmentation from computed tomography scans: a survey and a new algorithm. Artif Intell Med, 2009, 45(2-3): 185-196.
7. Işın A, Direkoğlu C, Şah M. Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Computer Science, 2016, 102: 317-324.
8. Grosu A L, Weber W A. PET for radiation treatment planning of brain tumours. Radiotherapy & Oncology, 2010, 96(3): 325-327.
9. Ibtehaz N, Rahman M S. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks, 2020, 121: 74-87.
10. Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Medical Image Analysis, 2017, 35: 18-31.
11. Myronenko A. 3D MRI brain tumor segmentation using autoencoder regularization// International MICCAI Brainlesion Workshop. Cham: Springer, 2018: 311-320.
12. Dalca A V, Guttag J, Sabuncu M R. Anatomical priors in convolutional networks for unsupervised biomedical segmentation//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 9290-9299.
13. Chen Liang, Bentley P, Rueckert D. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. Neuroimage: Clinical, 2017, 15: 633-643.
14. Hussain S, Anwar S M, Majid M. Segmentation of glioma tumors in brain using deep convolutional neural network. Neurocomputing, 2018, 282: 248-261.
15. Brügger R, Baumgartner C F, Konukoglu E. A partially reversible U-Net for memory-efficient volumetric image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention, Cham: Springer, 2019: 429-437.
16. Wang G, Li W, Ourselin S, et al. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks//International Conference on Medical Image Computing and Computer-Assisted Intervention, Cham: Springer, 2017: 178-190.
17. Mlynarski P, Delingette H, Criminisi A, et al. 3D convolutional neural networks for tumor segmentation using long-range 2D context. Computerized Medical Imaging and Graphics, 2019, 73: 60-72.
18. Wang G, Li W, Zuluaga M A, et al. Interactive medical image segmentation using deep learning with image-specific fine-tuning. IEEE Trans Med Imaging, 2018, 37(7): 1562-1573.
19. Zhou Chenhong, Chen Shengcong, Ding Changxing, et al. Learning contextual and attentive information for brain tumor segmentation// International MICCAI Brainlesion Workshop. Cham: Springer, 2018: 497-507.
20. Kao P Y, Chen J W, Manjunath B S. Improving 3D U-Net for brain tumor segmentation by utilizing lesion prior. Computer Vision and Pattern Recognition, 2019. arXiv: 1907.00281.
21. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention, Cham: Springer, 2015: 234-241.
22. Kayalibay B, Jensen G, van der Smagt P. CNN-based segmentation of medical imaging data. Computer Vision and Pattern Recognition, 2017. arXiv: 1701.03056.
23. Mckinley R, Meier R, Wiest R. Ensembles of densely-connected cnns with label-uncertainty for brain tumor segmentation// International MICCAI Brainlesion Workshop. Cham: Springer, 2018: 456-465.
24. Isensee F, Kickingereder P, Wick W, et al. No New-Net// International MICCAI Brainlesion Workshop. Cham: Springer, 2018: 234-244.
25. Liu Y, Stojadinovic S, Hrycushko B, et al. A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery. PLoS One, 2017, 12(10): e0185844.
26. Chen H, Dou Q, Yu L, et al. VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage, 2018, 170: 446-455.
27. Kamnitsas K, Bai Wenjia, Ferrante E, et al. Ensembles of multiple models and architectures for robust brain tumour segmentation// International MICCAI Brainlesion Workshop. Cham: Springer, 2017: 450-462.
28. Iqbal S, Khan M G, Saba T, et al. Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation. Microsc Res Tech, 2019, 82(8): 1302-1315.
29. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 39(4): 640-651.
30. Kamnitsas K, Ledig C, Newcombe V F J, et al. Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical Image Analysis, 2017, 36: 61-78.
31. Zhao Xiaomei, Wu Yihong, Song Guidong, et al. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Medical Image Analysis, 2018, 43: 98-111.
32. Xue Y, Xu T, Zhang H, et al. SegAN: adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics, 2018, 16(3-4): 383-392.
33. Soltaninejad M, Zhang Lei, Lambrou T, et al. Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network. Computer Vision and Pattern Recognition, 2017. arXiv: 1704.08134.
34. Li T, Zhou F, Zhu Z, et al. A label-fusion-aided convolutional neural network for isointense infant brain tissue segmentation//IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018: 692-695.
35. Sharma M, Purohit G N, Mukherjee S. Information retrieves from brain MRI images for tumor detection using hybrid technique K-means and artificial neural network (KMANN)// Networking Communication and Data Knowledge Engineering. Singapore: Springer, 2018: 145-157.
36. Ahmmed R, Swakshar A S, Hossain M F, et al. Classification of tumors and it stages in brain MRI using support vector machine and artificial neural network// 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE). Cox's Bazar: IEEE, 2017: 229-234.
37. Mohsen H, El-Dahshan E A, El-Horbaty E M, et al. Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 2018, 3(1): 68-71.

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Research on brain image segmentation based on deep learning

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