[1] Zubair M, Rana I A, Islam Y et al. Variable structure based control for the chemotherapy of brain tumor[J]. IEEE Access, 9, 107333-107346(2021).

[2] Kong Y Y, Li J H, Wang Y et al. Brain tumor segmentation method based on Hough transform and GVF Snake model[J]. Application Research of Computers, 35, 3469-3471, 3475(2018).

[3] Bray F, Ferlay J, Soerjomataram I et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: a Cancer Journal for Clinicians, 68, 394-424(2018).

[4] Li Q, Bai K X, Zhao L et al. Progresss and challenges of MRI brain tumor image segmentation[J]. Journal of Image and Graphics, 25, 419-431(2020).

[5] Bauer S, Wiest R, Nolte L P et al. A survey of MRI-based medical image analysis for brain tumor studies[J]. Physics in Medicine and Biology, 58, R97-R129(2013).

[6] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[7] Deng J, Dong W, Socher R et al. ImageNet: a large-scale hierarchical image database[C], 248-255(2019).

[8] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[9] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

[10] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C](2015).

[11] Jiang Z Y, Ding C X, Liu M F et al. Two-stage cascaded U-net: 1st place solution to BraTS challenge 2019 segmentation task[M]. Crimi A, Bakas S. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 11992, 231-241(2020).

[12] Zhao Y X, Zhang Y M, Liu C L. Bag of tricks for 3D MRI brain tumor segmentation[M]. Crimi A, Bakas S. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 11992, 210-220(2020).

[13] McKinley R, Rebsamen M, Meier R et al. Triplanar ensemble of 3D-to-2D CNNs with label-uncertainty for brain tumor segmentation[M]. Crimi A, Bakas S. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 11992, 379-387(2020).

[14] Chen M L, Wu Y Z, Wu J H. Aggregating multi-scale prediction based on 3D U-Net in brain tumor segmentation[M]. Crimi A, Bakas S. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 11992, 141-152(2020).

[15] González S R, Sekou T B, Hidane M et al. 3D automatic brain tumor segmentation using a multiscale input U-net network[M]. Crimi A, Bakas S. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 11992, 113-123(2020).

[16] Xu X W, Zhao W Y, Zhao J. Brain tumor segmentation using attention-based network in 3D MRI images[M]. Crimi A, Bakas S. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 11992, 3-13(2020).

[17] Bhalerao M, Thakur S. Brain tumor segmentation based on 3D residual U-net[M]. Crimi A, Bakas S. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 11992, 218-225(2020).

[18] Çiçek Ö, Abdulkadir A, Lienkamp S S et al. 3D U-net: learning dense volumetric segmentation from sparse annotation[M]. Ourselin S, Joskowicz L, Sabuncu M R, et al. Medical image computing and computer-assisted intervention-MICCAI 2016. Lecture notes in computer science, 9901, 424-432(2016).

[19] Chen Y P, Kalantidis Y, Li J S et al. Multi-fiber networks for video recognition[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11205, 364-380(2018).

[20] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[21] Wang G T, Li W Q, Ourselin S et al. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks[M]. Crimi A, Bakas S, Kuijf H, et al. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 10670, 178-190(2018).

[22] Sudre C H, Li W Q, Vercauteren T et al. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations[M]. Cardoso M J, Arbel T, Carneiro G, et al. Deep learning in medical image analysis and multimodal learning for clinical decision support. Lecture notes in computer science, 10553, 240-248(2017).

[23] He K M, Zhang X Y, Ren S Q et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification[C], 1026-1034(2015).

[24] Chen C, Liu X P, Ding M et al. 3D dilated multi-fiber network for real-time brain tumor segmentation in MRI[M]. Shen D G, Liu T M, Peters T M, et al. Medical image computing and computer assisted intervention-MICCAI 2019. Lecture notes in computer science, 11766, 184-192(2019).