[1] Işın A. Direko lu C, Şah M. Review of MRI-based brain tumor image segmentation using deep learning methods[J]. Procedia Computer Science, 102, 317-324(2016).
[2] Gragert M N, Antonini T N, Kahalley L S. Neuropsychological late effects of radiotherapy for pediatric brain tumors[M]. ∥Mahajan A, Paulino A, et al. Radiation Oncology for Pediatric CNS Tumors. Cham: Springer, 507-535(2018).
[3] Akter M K, Khan S M, Azad S et al. Automated brain tumor segmentation from mri data based on exploration of histogram characteristics of the cancerous hemisphere. [C]∥2017 IEEE Region 10 Humanitarian Technology Conference, December 21-23, 2017, Dhaka, Bangladesh. New York: IEEE, 815-818(2017).
[5] Pereira S, Pinto A, Alves V et al. Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI[M]. ∥Crimi A, Menze B, Maier O. et al. Computer Vision-ECCV 2016. Lecture Notes in Computer Science. Cham: Springer, 9556, 131-143(2016).
[6] Havaei M, Davy A, Warde-Farley D et al. Brain tumor segmentation with deep neural networks[J]. Medical Image Analysis, 35, 18-31(2017).
[7] Zhao L Y, Jia K B. Deep feature learning with discrimination mechanism for brain tumor segmentation and diagnosis. [C]∥2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, September 23-25, 2015, Adelaide, SA, Australia. New York: IEEE, 306-309(2015).
[8] de Brebisson A, Montana G. Deep neural networks for anatomical brain segmentation. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, June 7-12, 2015, Boston, MA, USA. New York: IEEE, 847-850(2015).
[9] Brosch T, Yoo Y. Tang L Y W, et al. Deep convolutional encoder networks for multiple sclerosis lesion segmentation[M]. ∥Navab N, Hornegger J, Wells W, et al. Computer Vision-ECCV 2015. Lecture Notes in Computer Science. Cham: Springer, 9351, 3-11(2015).
[10] G. Urban, M. Bendszus, F. Hamprecht et al[2019-09-18]. Multi-modal brain tumor segmentation using deep convolutional neural networks [2019-09-18].https:∥www.researchgate.net/publication/306204701_Multi-modal_brain_tumor_segmentation_using_deep_convolution.
[11] Kamnitsas K, Ledig C. Newcombe V F J, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation[J]. Medical Image Analysis, 36, 61-78(2017).
[12] Yu F, Koltun V[2019-09-23]. Multi-scale context aggregation by dilated convolutions [2019-09-23].https:∥arxiv., org/abs/1511, 07122.
[13] Gu Y, Lü X Q, Li J et al. Multimodal 3D convolutional neural networks for brain glioma segmentation[J]. Science Technology and Engineering, 18, 18-24(2018).
[15] Lu Y S, Chen W F. Research of the multimodal brain-tumor segmentation algorithm[J]. Proceedings of SPIE, 9814, 98140I(2015).
[16] Bakas S, Akbari H, Sotiras A et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features[J]. Scientific Data, 4, 170117(2017).
[17] Bakas S, Reyes M, Jakab A et al[2019-09-18]. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge [2019-09-18].https:∥arxiv., org/abs/1811, 02629.
[18] Nguyen D, Jia X, Sher D et al. 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-Net deep learning architecture[J]. Physics in Medicine & Biology, 64, 065020(2019).
[19] Wang Y, Zu C, Ma Z Q et al. Patch-wise label propagation for MR brain segmentation based on multi-atlas images[J]. Multimedia Systems, 25, 73-81(2019).
[20] Simonyan K, Zisserman A[2019-09-29]. Very deep convolutional networks for large-scale image recognition [2019-09-29].https:∥arxiv., org/abs/1409, 1556.
[21] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. ∥Navab N, Hornegger J, Wells W, et al. Computer Vision-ECCV 2015. Lecture Notes in Computer Science. Cham: Springer, 9351, 234-241(2015).
[22] Chen L, Papandreou G, Schroff F et al[2019-09-28]. Rethinking atrous convolution for semantic image segmentation [2019-09-28].https:∥arxiv., org/abs/1706, 05587.
[23] Wang P Q, Chen P F, Yuan Y et al. Understanding convolution for semantic segmentation. [C]∥2018 IEEE Winter Conference on Applications of Computer Vision, March 12-15, 2018, Lake Tahoe, NV. New York: IEEE, 1, 1451-1460(2018).
[24] Larsson G, Maire M, Shakhnarovich G[2019-09-23]. FractalNet: ultra-deep neural networks without residuals [2019-09-23].https:∥arxiv., org/abs/1605, 07648.
[25] Gu Y, Lu X Q, Yang L D et al. Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs[J]. Computers in Biology and Medicine, 103, 220-231(2018).
[26] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).
[27] Gu Y, Lu X Q, Zhang B H et al. Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography[J]. PLOS ONE, 14, e0210551(2019).
[32] Dong H, Yang G, Liu F D et al. Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks[M]. ∥Valdés Hernández M, González-Castro V, et al. Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, Cham: Springer, 723, 506-517(2017).
[33] Kermi A, Mahmoudi I, Khadir M T. Deep convolutional neural networks using U-Net for automatic brain tumor segmentation in multimodal MRI volumes[M]. ∥Crimi A, Bakas S, Kuijf H, et al. Computer Vision-ECCV 2019. Lecture Notes in Computer Science. Cham: Springer, 11384, 37-48(2019).
[34] Pereira S, Pinto A, Alves V et al. Brain tumor segmentation using convolutional neural networks in MRI images[J]. IEEE Transactions on Medical Imaging, 35, 1240-1251(2016).