[1] MENZE B H, JAKABA, BAUER S, et al. The multimodal brain tumor image segmentation benchmark (BRATS)[J]. IEEE Trans Med Imaging, 2015, 34(10):1993-2024. [2] DENG A, WU J, YANG S. An image fusion algorithm based on discrete wavelet transform and canny operator[M]. Berlin Heidelberg:Springer, 2011. [3] LEVINE M D, SHAHEEN S I. A modular computer vision system for picture segmentation and interpretation[J]. IEEE Trans Pattern Anal Mach Intell, 1981, 3(5):540-556. [4] SHIH F Y, CHENG S. Automatic seeded region growing for color image segmentation[M]. London:Butterworth-Heinemann, 2005. [5] YAMASAKI T. GrowCut-based fast tumor segmentation for 3D magnetic resonance images[C]//Medical Imaging:Image Processing. San Diego, California, USA:International Society for Optics and Photonics, 2012. [6] RANA R, BHADAURIA H S, SINGH A. Brain tumour extraction from MRI images using bounding-box with level set method[C]//Sixth International Conference on Contemporary Computing. Noida, India:IEEE, 2013. [7] SELVAKUMAR J, LAKSHMI A, Arivoli T. Brain tumor segmentation and its area calculation in brain MR images using K-mean clustering and Fuzzy C-mean algorithm[C]//International Conference on Advances in Engineering. Singapore:IEEE, 2012. [8] CLARKE L P, VELTHUIZEN R P, CAMACHO M A, et al. MRI segmentation:methods and applications[J]. Magn Reson Imaging, 1995, 13(3):343-368. [9] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553):436-444. [10] ZHANG W L, LI R J, DENG H T, et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation[J]. Proc IEEE Int Symp Biomed Imaging, 2015, 108:1342-1345. [11] LIAO S, GAO Y Z, OTO A, et al. Representation learning:A unified deep learning framework for automatic prostate MR segmentation[J]. Med Image Comput Comput Assist Interv, 2013, 16(2):254-261. [12] GUO Y R, GAO Y Z, SHEN D G. Deformable MR prostate segmentation via deep feature learning and sparse patch matching[J]. IEEE Trans Med Imaging, 2016, 35(4):1077-1089. [13] WU G R, KIM M, WANG Q, et al. Unsupervised deep feature learning for deformable registration of MR brain images[J]. Med Image Comput Comput Assist Interv, 2013, 16(2):649-656. [14] XIAO Z, HUANG R H, DING Y, et al. A deep learning-based segmentation method for brain tumor in MR images[C]//2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). Atlanta, Georgia, USA:IEEE, 2016. [15] WANG H Z, ZHAO D, YANG L Q, et al. An approach for training data enrichment and batch labeling in AI+MRI aided diagnosis[J]. Chinese J Magn Reson, 2018(4):447-456. 汪红志, 赵地, 杨丽琴, 等. 基于AI+MRI的影像诊断的样本增广与批量标注方法[J]. 波谱学杂志, 2018(4):447-456. [16] HAVAEI M, DAVY A, WARDE-FARLEY D, et al. Brain tumor segmentation with deep neural networks[J]. Med Image Anal, 2015, 35:18-31. [17] PEREIRA S, PINTO A, ALVES V, et al. Brain tumor segmentation using convolutional neural networks in MRI images[J]. IEEE Trans Med Imaging, 2016, 35(5):1240-1251. [18] 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. Munich, Germany:Springer, 2015.9351:234-241. [19] 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]. Med Image Anal, 2017, 36:61-78. [20] CASAMITJANA A, PUCH S, ADURIZ A, et al. 3D convolutional neural networks for brain tumor segmentation:a comparison of multi-resolution architectures[C]//International Workshop on Brainlesion:Glioma. Quebec, Canada:Springer, 2017. [21] CHEN L L, WU Y, DSOUZA A M, et al. MRI tumor segmentation with densely connected 3D CNN[C]//Medical Imaging 2018:Image Processing. Houston, Texas, United States:International Society for Optics and Photonics, 2018, 10574:105741F. [22] MEHTA R, ARBEL T. RS-Net:Regression-segmentation 3D CNN for synthesis of full resolution missing brain MRI in the presence of tumours[C]//International Workshop on Simulation and Synthesis in Medical Imaging. Granada, Spain:Springer, 2018. [23] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA:IEEE, 2016:2818-2826. [24] IOFFE S, SZEGEDY C. Batch normalization:Accelerating deep network training by reducing internal covariate shift[J]. ICML, 2015. [25] CIAMPI M. Medical image fusion for color visualization via 3D RDWT[C]//IEEE International Conference on Information Technology & Applications in Biomedicine. Corfu, Greece:IEEE, 2010. [26] CHAI Q H, SU G Q, NIE S D. Compressive sensing low-field MRI reconstruction with dual-tree wavelet transform and wavelet tree sparsity[J]. Chinese J Magn Reson, 2018, 35(4):486-497. 柴青焕, 苏冠群, 聂生东. 双树小波变换与小波树稀疏联合的低场CS-MRI算法[J]. 波谱学杂志, 2018, 35(4):486-497. [27] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout:A simple way to prevent neural networks from overfitting[J]. J Mach Learn Res, 2014, 15(1):1929-1958. [28] BOCK S, GOPPOLD J, WEI M. An improvement of the convergence proof of the ADAM-optimizer[J]. Computer Science, 2018. arXiv:1804.10587 [29] TUSTION N J, AVANTS B B, COOK P A, et al. N4ITK:improved N3 bias correction[J]. IEEE Trans Med Imaging, 2010, 29(6):1310-1320. [30] KAO P Y, NGO T, ZHANG A, et al. Brain tumor segmentation and tractographic feature extraction from structural MR images for overall survival prediction[C]//International MICCAI Brainlesion Workshop. Granada, Spain:Springer, 2018. [31] WENINGER L, RIPPEL O, KOPPERS S, et al. Segmentation of brain tumors and patient survival prediction:methods for the BraTS 2018 challenge[C]//International MICCAI Brainlesion Workshop. Granada, Spain:Springer, 2018. |