[1] 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, 1-13(2017).

[2] QI Xingbin, ZHAO Li, LI Xuemei et al. Automatic segmentation of T2 weighted brain MRI based on histogram gradient calculation[J]. Application Research of Computers, 32, 1576-1579(2015).

[3] NEMA S, DUDHANE A, MURALA S et al. RescueNet:AN unpaired GAN for brain tumor segementation[J]. Biomedical Signal Processing and Control, 55, 101641(2020).

[4] ZHANG Y, MATUSZEWSKI B J, SHARK L K. A novel medical image segmentation mentation method using dynamic programming[C], 69-74(2007).

[5] LONG J, SHELHAMER E, DARRELL T. Fully convolution networks for semantic segmentation[C], 3431-3440(2015).

[6] RONNEBERGER O, FISCHER P, BROX T. U-Net:Convolutional networks for biomedical image segmentation[C], 234-241(2015).

[7] ZHOU Tao, DONG Yali, LIU Shan et al. Cross-modality multi-encoder hybrid attention U-Net for lung tumors images segmentation[J]. Acta Photonica Sinica, 51, 376-392(2022).

[8] HUANG Hong, Rongfei LÜ, TAO Junli et al. Segmentation of lung nodules in CT images using improved UNet++[J]. Acta Photonica Sinica, 50, 73-83(2021).

[9] ÇICEK Ö, ABDULKADIR A, LIENKAMP S S et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation[C], 424-432(2016).

[10] SUN J, PENG Y, LI D et al. Segmentation of the multimodal brain tumor images used Res-U-Net[C], 263-273(2021).

[11] XIA Feng, SHAO Haijian, DENG Xing. Cross-stage deep-learning-based MRI fused images of human brain tumor segmentation[J]. Journal of Image and Graphics, 27, 873-884(2022).

[12] MCKINLEY R, REBSAMEN M, DÄTWYLER K et al. Uncertainty-driven refinement of tumor-core segmentation using 3d-to-2d networks with label uncertainty[C], 401-411(2021).

[13] PEI L, MURAT A K, COLEN R. Multimodal brain tumor segmentation and survival prediction using a 3D self-ensemble ResUNet[C], 367-375(2021).

[14] ZHANG Xiaoyu, WANG Bin, AN Weichao et al. Glioma segmentation network based on 3DU-Net++ with fusion loss function[J]. Computer Science, 48, 187-193(2021).

[15] WANG W, CHEN C, DING M et al. Transbts: multimodal brain tumor segmentation using transformer[C], 109-119(2021).

[16] XUE Y, XU T, ZHANG H et al. SegAN:adversarial network with multi-scale L1 loss for medical image segmentation[J]. Neuroinformatics, 382-392(2018).

[17] HAMGHALAM M, LEI B, WANG T. Brain tumor synthetic segmentation in 3D multimodal MRI scans[C], 153-162(2020).

[18] CIRILLO M D, ABRAMIAN D, EKLUND A. Vox2Vox: 3D-GAN for brain tumour segmentation[C], 274-284(2021).

[19] NEMA S, DUDHANE A, MURALA S et al. RescueNet: an unpaired GAN for brain tumor segmentation[J]. Biomedical Signal Processing and Control, 55, 101641(2020).

[20] ISOLA P, ZHU J Y, ZHOU T et al. Image-to-image translation with conditional adversarial networks[C], 1125-1134(2017).

[21] ZHU J Y, PARK T, ISOLA P et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C], 2223-2232(2017).

[22] DING Y, ZHANG C, CAO M et al. ToStaGAN: an end-to-end two-stage generative adversarial network for brain tumor segmentation[J]. Neurocomputing, 462, 141-153(2021).

[23] PEIRIS H, CHEN Z, EGAN G et al. Reciprocal adversarial learning for brain tumor segmentation: a solution to BraTS challenge 2021 segmentation task[C], 171-181(2022).

[24] HAN K, WANG Y, TIAN Q et al. Ghostnet: more features from cheap operations[C], 1580-1589(2020).

[25] HOWARD A G, ZHU M, CHEN B et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[J]. arXiv Preprint(2017).