[1] Işın A. Direko lu C, Şah M. Review of MRI-based brain tumor image segmentation using deep learning methods[J]. Procedia Computer Science, 2016, 102: 317-324.

[2] Gragert MN, Antonini TN, Kahalley LS. Neuropsychological late effects of radiotherapy for pediatric brain tumors[M] ∥Mahajan A, Paulino A, et al. Radiation Oncology for Pediatric CNS Tumors. Cham: Springer, 2018: 507- 535.

[3] Akter MK, Khan SM, AzadS, 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, 2017: 815- 818.

[4] 李仁忠, 刘阳阳, 杨曼, 等. 基于改进的区域生长三维点云分割[J]. 激光与光电子学进展, 2018, 55(5): 051502.

    Li R Z, Liu Y Y, Yang M, et al. Three-dimensional point cloud segmentation algorithm based on improved region growing[J]. Laser & Optoelectronics Progress, 2018, 55(5): 051502.

[5] PereiraS, PintoA, AlvesV, 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, 2016, 9556: 131- 143.

[6] Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks[J]. Medical Image Analysis, 2017, 35: 18-31.

[7] Zhao LY, Jia KB. 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, 2015: 306- 309.

[8] de BrebissonA, MontanaG. 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, 2015: 847- 850.

[9] BroschT, YooY, 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, 2015, 9351: 3- 11.

[10] G.Urban, M.Bendszus, F.Hamprecht, et al. Multi-modal brain tumor segmentation using deep convolutional neural networks[EB/OL]. [2019-09-18].https:∥www.researchgate.net/publication/306204701_Multi-modal_brain_tumor_segmentation_using_deep_convolutional_neural_networks.

[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, 2017, 36: 61-78.

[12] YuF, KoltunV. Multi-scale context aggregation by dilated convolutions[EB/OL]. [2019-09-23].https:∥arxiv.org/abs/1511. 07122

[13] 谷宇, 吕晓琪, 李菁, 等. 多模态3D卷积神经网络脑部胶质瘤分割方法[J]. 科学技术与工程, 2018, 18(7): 18-24.

    Gu Y, Lü X Q, Li J, et al. Multimodal 3D convolutional neural networks for brain glioma segmentation[J]. Science Technology and Engineering, 2018, 18(7): 18-24.

[14] 任璐, 李锵, 关欣, 等. 改进的连续型最大流算法脑肿瘤磁核共振成像三维分割[J]. 激光与光电子学进展, 2018, 55(11): 111011.

    Ren L, Li Q, Guan X, et al. Three-dimensional segmentation of brain tumors in magnetic resonance imaging based on improved continuous max-flow[J]. Laser & Optoelectronics Progress, 2018, 55(11): 111011.

[15] Lu Y S, Chen W F. Research of the multimodal brain-tumor segmentation algorithm[J]. Proceedings of SPIE, 2015, 9814: 98140I.

[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, 2017, 4: 170117.

[17] BakasS, ReyesM, JakabA, et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge[EB/OL]. [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, 2019, 64(6): 065020.

[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, 2019, 25(2): 73-81.

[20] SimonyanK, ZissermanA. Very deep convolutional networks for large-scale image recognition[EB/OL]. [2019-09-29].https:∥arxiv.org/abs/1409. 1556.

[21] RonnebergerO, FischerP, BroxT. 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, 2015, 9351: 234- 241.

[22] ChenL, PapandreouG, SchroffF, et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. [2019-09-28].https:∥arxiv.org/abs/1706. 05587.

[23] Wang PQ, Chen PF, YuanY, 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, 2018, 1: 1451- 1460.

[24] LarssonG, MaireM, ShakhnarovichG. FractalNet: ultra-deep neural networks without residuals[EB/OL]. [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, 2018, 103: 220-231.

[26] He KM, Zhang XY, Ren SQ, 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, 2016: 770- 778.

[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, 2019, 14(1): e0210551.

[28] 王得成, 陈向宁, 易辉, 等. 基于自适应联合双边滤波的深度图像空洞填充与优化算法[J]. 中国激光, 2019, 46(10): 1009002.

    Wang D C, Chen X N, Yi H, et al. Hole filling and optimization algorithm for depth images based on adaptive joint bilateral filtering[J]. Chinese Journal of Lasers, 2019, 46(10): 1009002.

[29] 尚秋峰, 秦文婕, 胡雨婷. 基于Armijo线搜索的布里渊散射谱图像降噪算法[J]. 中国激光, 2019, 46(9): 0906002.

    Shang Q F, Qin W J, Hu Y T. Brillouin scattering spectral image denoising algorithm based on Armijo line search[J]. Chinese Journal of Lasers, 2019, 46(9): 0906002.

[30] 毕晓君, 周泽宇. 基于双通道GAN的高光谱图像分类算法[J]. 光学学报, 2019, 39(10): 1028002.

    Bi X J, Zhou Z Y. Hyperspectral image classification algorithm based on two-channel generative adversarial network[J]. Acta Optica Sinica, 2019, 39(10): 1028002.

[31] 胡涛, 李卫华, 秦先祥. 基于多层深度特征融合的极化合成孔径雷达图像语义分割[J]. 中国激光, 2019, 46(2): 0210001.

    Hu T, Li W H, Qin X X. Semantic segmentation of polarimetric synthetic aperture radar images based on multi-layer deep feature fusion[J]. Chinese Journal of Lasers, 2019, 46(2): 0210001.

[32] DongH, YangG, Liu FD, 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, 2017, 723: 506- 517.

[33] KermiA, MahmoudiI, Khadir MT. 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, 2019, 11384: 37- 48.

[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, 2016, 35(5): 1240-1251.

[35] 褚晶辉, 李晓川, 张佳祺, 等. 一种基于级联卷积网络的三维脑肿瘤精细分割[J]. 激光与光电子学进展, 2019, 56(10): 101001.

    Chu J H, Li X C, Zhang J Q, et al. Fine-granted segmentation method for three-dimensional brain tumors using cascaded convolutional network[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101001.