[1] [1] Huan Z, Zhao Y, Liu Y, et al. GCAUNet: A group cross-channel attention residual UNet for slice based brain tumor segmentation［J］. Biomedical Signal Processing and Control,2021,70:102958.

[3] [3] Kaur T, Saini B S, Gupta S. A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation［J］. Australasian Physical & Engineering Sciences in Medicine / Supported by The Australasian College of Physical Scientists in Medicine and the Australasian Association of Physical Sciences in Medicine,2017,41(1):1-18.

[4] [4] Weglinski T, Fabijanska A . Brain tumor segmentation from MRI data sets using region growing approach［C］∥Perspective Technologies and Methods in MEMS Design,2011:185-188.

[8] [8] Sharma N, Ray A K. Computer aided segmentation of medical images based on hybridized approach of edge and region based techniques［C］∥Proceedings of International Conference on Mathematical Biology’, Mathematical Biology Recent Trends by Anamaya Publishers,2006:150-155.

[10] [10] Long J, Shelhamer E, Darrell T. Fully Convolutional Networks for semantic segmentation［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,39(4):640-651.

[11] [11] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks［J］. Advances in Neural Information Processing Systems,2012,25:1097-1105.

[12] [12] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition［J］. arXiv Preprint arXiv:1409.1556,2014.

[13] [13] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions［J］. 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston:IEEE,2015:1-9.

[14] [14] Ronneberger O , Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation［C］∥International Conference on Medical Image Computing and computer-assisted Intervention. Switzerland:Springer,Cham,2015:231-241.

[15] [15] Hu Y, Liu X, Wen X, et al. Brain tumor segmentation on multimodal mr imaging using multi-level upsampling in decoder［C］∥International MICCAI Brainlesion Workshop. Xi’an, China: Springer,Cham,2018:168-177.

[16] [16] Alom M Z, Yakopcic C, Hasan M, et al. Recurrent residual U-Net for medical image segmentation［J］. Journal of Medical Imaging,2019,6(1):014006.

[17] [17] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation［C］∥Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition. USA:IEEE,2014:580-587.

[18] [18] Jiang Y, Zhu X, Wwang X, et al. R2cnn: rotational region cnn for orientation robust scene text detection［J］. arXiv Preprint arXiv:1706.09579,2017.

[19] [19] Chen Y, Ccao Z, Ccao C, et al.A modified U-Net for brain Mr image segmentation［C］∥International Conference on Cloud Computing and Security.Nanjing,China:Springer,Cham,2018:233-242.

[20] [20] Buda M, Saha A, Mazurowski M A. Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm［J］. Computers in Biology and Medicine,2019,109:218-225.

[24] [24] Wang H, Cao P, Wang J, et al. UCTransNet: Rethinking the skip connections in U-Net from a Channel-wise perspective with transformer［J］. arXiv preprint arXiv:2109.04335,2021.

[25] [25] 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.

[27] [27] Jia H, Cai W, Huang H, et al. H2NF-Net for brain tumor segmentation using multimodal MR imaging: 2nd place solution to BraTS challenge 2020 segmentation task［J］. arXiv preprint arXiv:2012.15318,2020.

[28] [28] Wang Y, Zhang Y, Hou F, et al. Modality-pairing learning for brain tumor segmentation［J］. arXiv preprint arXiv:2010.09277,2020.

[29] [29] Isensee F, Jger P F, Full P M, et al. nnU-net for brain tumor segmentation［C］∥International MICCAI Brainlesion Workshop. Heidelberg, Germany:Springer,Cham,2020:118-132.