[1] 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.

[2] 张祥甫, 刘健, 石章松, 等. 基于深度学习的语义分割问题研究综述[J]. 激光与光电子学进展, 2019, 56(15): 150003.

    Zhang X F, Liu J, Shi Z S, et al. Review of deep learning-based semantic segmentation[J]. Laser & Optoelectronics Progress, 2019, 56(15): 150003.

[3] ShelhamerE, LongJ, DarrellT. Fully convolutional networks for semantic segmentation[C] //IEEE Transactions on Pattern Analysis and Machine Intelligence,New York: IEEE Press, 2015: 640- 651.

[4] RonnebergerO, FischerP, BroxT. U-net: convolutional networks for biomedical image segmentation[M] //Navab N, Hornegger J, Wells W, et al. Medical Image Computing and Computer-Assisted Intervention Cham: Springer, 2015, 9351: 234- 241.

[5] MilletariF, NavabN, Ahmadi SA. V-net: fully convolutional neural networks for volumetric medical image segmentation[C] //2016 Fourth International Conference on 3D Vision (3DV), October 25-28, 2016, Stanford, CA, USA. New York: IEEE Press, 2016: 565- 571.

[6] Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848.

[7] YuF, KoltunV. Multi-scale context aggregation by dilated convolutions[EB/OL]. (2016-04-30)[2020-08-17]. https://arxiv.org/abs/1511.07122.

[8] 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.

[9] Chen LC, PapandreouG, SchroffF, et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. (2017-06-17)[2020-08-17]. https://arxiv.org/abs/1706.05587v1.

[10] Chen LC, Zhu YK, PapandreouG, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C] //Ferrari V, Hebert M, Sminchisescu C, et al. Computer Vision-ECCV 2018. Cham: Springer, 2018, 11211: 833- 851.

[11] Wang GT, Li WQ, OurselinS, et al. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks[EB/OL]. 2017[2020-08-17]. https:∥arxiv.org/pdf/1709.00382.pdf.

[12] MyronenkoA. 3D MRI brain tumor segmentation using autoencoder regularization[C] // Crimi A, Bakas S, Kuijf H, et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer, 2018, 11384: 311- 320.

[13] MalmiE, ParambathS, Peyrat JM, et al. CaBS: a cascaded brain tumor segmentation approach[C] //Proceedings MICCAI Brain, Tumor Segmentation (BRATS). [S.l.: s.n.], 2015: 42- 47.

[14] 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.

[15] LaffertyJ, MccallumA, PereiraF, et al. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C] // Proceedings of 18th International Conference on International Conference on Machine Learning, June 28-July 1, 2001, Williamstown, MA, USA. [S. l.]: International Machine Learning Society, 2001: 282- 289.

[16] ZhengS, JayasumanaS, Romera-ParedesB, et al.Conditional random fields as recurrent neural networks[C] //2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile.New York: IEEE Press, 2015: 1529- 1537.

[17] IoffeS, SzegedyC, Batch normalization: accelerating deep network training by reducing internal covariate shift[EB/OL]. (2015-03-02)[2020-08-17]. https://arxiv.org/abs/1502.03167.

[18] He KM, Zhang XY, Ren SQ, et al.Delving deep into rectifiers: surpassing human-level performance on ImageNet classification[C] //2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE Press, 2015: 1026- 1034.

[19] Menze B H, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS)[J]. IEEE Transactions on Medical Imaging, 2015, 34(10): 1993-2024.

[20] 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.

[21] AbadiM, BarhamP, Chen JM, et al. TensorFlow: a system for large-scale machine learning[EB/OL]. (2016-05-27)[2020-08-17]. https://arxiv.org/abs/1605.08695.

[22] Gibson E, Li W Q, Sudre C, et al. NiftyNet: a deep-learning platform for medical imaging[J]. Computer Methods and Programs in Biomedicine, 2018, 158: 113-122.

[23] Thada V, Jaglan D V. Comparison of jaccard, dice, cosine similarity coefficient to find best fitness value for web retrieved documents using genetic algorithm[J]. International Journal of Innovations in Engineering and Technology, 2013, 2(4): 202-205.

[24] Huttenlocher D P, Klanderman G A, Rucklidge W J. Comparing images using the Hausdorff distance[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(9): 850-863.

[25] IsenseeF, KickingerederP, WickW, et al. No new-net[EB/OL]. (2019-01-31)[2020-08-17]. https://arxiv.org/abs/1809.10483?context=cs.CV.

[26] McKinleyR, MeierR, WiestR. Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation[C] // Crimi A, Bakas S, Kuijf H, et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer, 2018, 11384: 456- 465.

[27] Wang G T, Li W Q, Ourselin S, et al. Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation[J]. Frontiers in Computational Neuroscience, 2019, 13: 56.

[28] MehtaR, ArbelT. 3D U-net for brain tumour segmentation[C] //Crimi A, Bakas S, Kuijf H, et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer, 2018, 11384: 254- 266.

[29] HuaR, HuoQ, Gao YZ, et al. Multimodal brain tumor segmentation using cascaded V-nets[M] //Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer, 2019: 49- 60.