[1] Louis D N, Perry A, Reifenberger G et al. The 2016 world health organization classification of tumors of the central nervous system: a summary[J]. Acta Neuropathologica, 131, 803-820(2016).
[3] Lai X B, Xu M S, Xu X M. Automatic segmentation for glioblastoma multiforme using multimodal MR images and multiple features[J]. Journal of Computer-Aided Design & Computer Graphics, 31, 421-430(2019).
[4] Li Y Z, Dai S G. Application of improved watershed algorithm in segmentation of brain tumor CT images[J]. Software Guide, 17, 157-159(2018).
[5] Jiang Q L, Wang X. Brain tumor image segmentation based on region growing algorithm[J]. Journal of Changchun University of Technology, 39, 490-493(2018).
[6] Zhang C J, Fang M C, Nie H H. Brain tumor segmentation using fully convolutional networks from magnetic resonance imaging[J]. Journal of Medical Imaging and Health Informatics, 8, 1546-1553(2018).
[7] Dong H, Yang G, Liu F D et al[M]. Automatic brain tumor detection and segmentation using U-net based fully convolutional networks, 506-517(2017).
[8] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 3431-3440(2015).
[9] Ronneberger O, Fischer P, Brox T[M]. U-net: convolutional networks for biomedical image segmentation, 234-241(2015).
[10] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).
[11] Huang G, Sun Y, Liu Z et al[M]. Deep networks with stochastic depth, 646-661(2016).
[12] Zeiler M D, Krishnan D, Taylor G W et al. Deconvolutional networks. [C]∥2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 13-18, 2010, San Francisco, CA, USA. New York: IEEE, 2528-2535(2010).
[13] Odena A, Dumoulin V, Olah C. Deconvolution, checkerboard artifacts[J/OL]. Distill. 2019-10-28]. http: ∥distill. pub/2016/deconv-checkerboard.(2016).
[14] Hu J, Shen L, Sun G. Squeeze-and-excitation networks. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT. New York: IEEE, 7132-7141(2018).
[15] Menze B, Jakab A, Bauer S et al. The multimodal brain tumor image segmentation benchmark (BRATS)[J]. IEEE Transactions on Medical Imaging, 34, 1993-2024(2015).
[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, 4, 170117(2017).
[17] Bakas S, Akbari H, Sotiras A et al. Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection [2019-11-30]. https: ∥doi. org/10. 7937/K9/[2019-11-30]. TCIA., KLXWJJ1Q(2017).
[18] Bakas S, Akbari H, Sotiras A et al. /K9/TCIA. 2017. GJQ7R0. EF.(7937).
[19] Tustison N J, Gee J C. N4ITK: Nick's N3 ITK implementation for MRI bias field correction[J]. Insight Journal, 1-8(2009).
[20] Sudre C H, Li W Q, Vercauteren T et al. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. [C]∥Deep learning in medical image analysis and multimodal learning for clinical decision support. Cham: Springer, 240-248(2017).
[21] Isensee F, Kickingereder P, Wick W et al[M]. Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge, 287-297(2018).
[22] Puch S, Sánchez I, Hernández A et al. Global planar convolutions for improved context aggregation in brain tumor segmentation. [C]∥ Crimi A, Bakas S, Kuijf H, et al. International MICCAI Brainlesion Workshop. Cham: Springer, 11384, 393-405(2019).
