• Laser & Optoelectronics Progress
  • Vol. 58, Issue 24, 2400003 (2021)
Zhiwei Li1, Hui Cao1, Feng Yang1, and Bin Cao2、*
Author Affiliations
  • 1School of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250355, China
  • 2Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, China
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    DOI: 10.3788/LOP202158.2400003 Cite this Article Set citation alerts
    Zhiwei Li, Hui Cao, Feng Yang, Bin Cao. Research Progress of Brain Tumor Segmentation Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2400003 Copy Citation Text show less
    References

    [1] Fitzmaurice C, Allen C, Barber R M et al. Global,regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the global burden of disease study[J]. JAMA Oncology, 3, 524-548(2017).

    [2] Yang F, Wei G H, Cao H et al. Research progress on content-based medical image retrieval[J]. Laser & Optoelectronics Progress, 57, 060003(2020).

    [3] Chen X C. Research on algorithm and application of deep learning based on convolutional neural network[D](2014).

    [4] Chen S H, Liu W X, Qin J et al. Research progress of computer-aided diagnosis in cancer based on deep learning and medical imaging[J]. Journal of Biomedical Engineering, 34, 314-319(2017).

    [5] Lei C, Ye X Y, Li X B. Deep learning technology and its application in tumor classification[J]. Intelligent Computer and Applications, 4, 17-19(2014).

    [6] Sun Z J, Xue L, Xu Y M et al. Overview of deep learning[J]. Application Research of Computers, 29, 2806-2810(2012).

    [7] Cui S M. Research on image segmentation algorithm based on multimodal MRI brain tumor[D](2019).

    [8] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]. //Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, December 3-6, 2012, Lake Tahoe, Nevada, United States. [S.l.:s.n.], 1106-1114(2012).

    [9] LeCun Y, Bottou L, Bengio Y et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998).

    [10] Havaei M, Davy A, Warde-Farley D et al. Brain tumor segmentation with deep neural networks[J]. Medical Image Analysis, 35, 18-31(2017).

    [11] Zikic D, Ioannou Y, Brown M et al. Segmentation of Brain Tumor Tissues with Convolutional Neural Networks[C]. // MICCAI workshop on Multimodal Brain Tumor Segmentation Challenge (BRATS)(2014).

    [12] Lyksborg M, Puonti O, Agn M et al. An ensemble of 2D convolutional neural networks for tumor segmentation[M]. //Paulsen R P, Pedersen K S. Image analysis. Lecture notes in computer science, 9127, 201-211(2015).

    [13] Pereira S, Pinto A, Alves V et al. Brain tumor segmentation using convolutional neural networks in MRI images[J]. IEEE Transactions on Medical Imaging, 35, 1240-1251(2016).

    [14] Mohseni Salehi S S, Erdogmus D, Gholipour A. Auto-context convolutional neural network (Auto-Net) for brain extraction in magnetic resonance imaging[J]. IEEE Transactions on Medical Imaging, 36, 2319-2330(2017).

    [15] Tseng K L, Lin Y L, Hsu W et al. Joint sequence learning and cross-modality convolution for 3D biomedical segmentation[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 3739-3746(2017).

    [16] 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).

    [17] Luo M, Huang J, Yang F. Multimodal 3D convolutional neural networks features for brain tumor segmentation[J]. Science Technology and Engineering, 14, 78-83(2014).

    [18] Kamnitsas K, Ferrante E, Parisot S et al. DeepMedic for brain tumor segmentation[M]. //Crimi A, Bakas S, Kuijf H, et al. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 10154, 138-149(2016).

    [19] Casamitjana A, Puch S, Aduriz A et al. 3D convolutional neural networks for brain tumor segmentation: a comparison of multi-resolution architectures[M]. //Crimi A, Bakas S, Kuijf H, et al. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 10154, 150-161(2016).

    [20] 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, 36, 61-78(2017).

    [21] Qamar S, Jin H, Zheng R et al. 3D hyper-dense connected convolutional neural network for brain tumor segmentation[C]. //2018 14th International Conference on Semantics, Knowledge and Grids (SKG), September 12-14, 2018, Guangzhou, China., 123-130(2018).

    [22] Chen L L, Wu Y, DSouza A M et al. MRI tumor segmentation with densely connected 3D CNN[J]. Proceedings of SPIE, 1057, 357-364(2018).

    [23] Urban G, Bendszus M, Hamprecht F A et al. Multi-modal brain tumor segmentation using deep convolutional neural networks[C]. //Proceedings of the MICCAI-Bra TSs.Boston, 31-35(2014).

    [24] Feng B W, Lü X Q, Gu Y et al. Three-dimensional parallel convolution neural network brain tumor segmentation based on dilated convolution[J]. Laser & Optoelectronics Progress, 57, 141009(2020).

    [25] Mlynarski P, Delingette H, Criminisi A et al. 3D convolutional neural networks for tumor segmentation using long-range 2D context[J]. Computerized Medical Imaging and Graphics, 73, 60-72(2019).

    [26] 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., 3431-3440(2015).

    [27] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2014-09-04)[2020-11-25]. https://arxiv.org/abs/1409.1556

    [28] Shen H C, Wang R X, Zhang J G et al. Multi-task fully convolutional network for brain tumour segmentation[M]. //Hernández M V, González-Castro V. Communications in computer and information science. Lecture notes in computer science, 723, 239-248(2017).

    [29] Shen H C, Zhang J G, Zheng W S. Efficient symmetry-driven fully convolutional network for multimodal brain tumor segmentation[C]. //2017 IEEE International Conference on Image Processing (ICIP), September 17-20, 2017, Beijing, China., 3864-3868(2017).

    [30] Zhao X M, Wu Y H, Song G D et al. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation[J]. Medical Image Analysis, 43, 98-111(2018).

    [31] Zhao X M, Wu Y H, Song G D et al. 3D brain tumor segmentation through integrating multiple 2D FCNNs[M]. //Crimi A, Bakas S, Kuijf H, et al. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 10670, 191-203(2018).

    [32] Li H M, Fan Y. Non-rigid image registration using fully convolutional networks with deep self-supervision[EB/OL]. (2017-09-04)[2020-11-25]. https://arxiv.org/abs/1709.00799

    [33] Pereira S, Pinto A, Amorim J et al. Adaptive feature recombination and recalibration for semantic segmentation with fully convolutional networks[J]. IEEE Transactions on Medical Imaging, 38, 2914-2925(2019).

    [34] Puch S, Sánchez I, Hernández A et al. Global planar convolutions for improved context aggregation in brain tumor segmentation[M]. //Crimi A, Bakas S, Kuijf H, et al. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 11384, 393-405(2019).

    [35] 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).

    [36] Dong H, Yang G, Liu F D et al. Automatic brain tumor detection and segmentation using U-net based fully convolutional networks[M]. //Hernández M V, González-Castro V. Medical image understanding and analysis. Communications in computer and information science, 723, 506-517(2017).

    [37] Wang H O, Liu H, Guo Q et al. Design of superpixel U-net network for medical image segmentation[J]. Journal of Computer-Aided Design & Computer Graphics, 31, 1007-1017(2019).

    [38] Shaikh M, Anand G, Acharya G et al. Brain tumor segmentation using dense fully convolutional neural network[M]. //Crimi A, Bakas S, Kuijf H, et al. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 10670, 309-319(2018).

    [39] Liu D, Zhang H, Zhao M M et al. Brain tumor segmention based on dilated convolution refine networks[C]. //2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA), June 13-15, 2018, Kunming, China., 113-120(2018).

    [40] Chen Y J, Cao Z H, Cao C Z et al. A modified U-net for brain MR image segmentation[M]. //Sun X M, Pan Z Q, Bertino E. Cloud computing and security. Lecture notes in computer science, 11068, 233-242(2018).

    [41] Isensee F, Kickingereder P, Bonekamp D et al. Brain tumor segmentation using large receptive field deep convolutional neural networks[M]. Maier-Hein K H, Fritzsche G, Deserno T M, et al. Bildverarbeitung Für Die Medizin 2017. Informatik aktuell, 86-91(2017).

    [42] Isensee F, Kickingereder P, Wick W et al. Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge[M]. //Crimi A, Bakas S, Kuijf H, et al. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 10670, 287-297(2018).

    [43] Kong X M, Sun G X, Wu Q et al. Hybrid pyramid U-net model for brain tumor segmentation[M]. //Shi Z Z, Mercier-Laurent E, Li J Y. IFIP advances in information and communication technology. IFIP advances in information and communication technology, 538, 346-355(2018).

    [44] Alom M Z, Hasan M, Yakopcic C et al. Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation[EB/OL]. (2018-02-20)[2020-11-25]. https://arxiv.org/abs/1802.06955

    [45] Ai L M, Li T D, Liao F Y et al. Magnetic resonance brain tumor image segmentation based on attention U-net[J]. Laser & Optoelectronics Progress, 57, 141030(2020).

    [46] Chu J H, Li X C, Zhang J Q et al. Fine-granted segmentation method for three-dimensional brain tumors using cascaded convolutional network[J]. Laser & Optoelectronics Progress, 56, 101001(2019).

    [47] Menze B, Jakab A, Bauer S et al. Proceedings of the MICCAI Challenge on Multimodal Brain Tumor Image Segmentation (BRATS) 2012[J]. Miccai Challenge on Multimodal Brain Tumor Image Segmentation(2013).

    [48] Beers A, Chang K, Brown J et al. Sequential 3D U-Nets for biologically-informed brain tumor segmentation[EB/OL]. (2017-09-09)[2020-11-25]. https://arxiv.org/abs/1709.02967

    [49] 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).

    [50] Kayalibay B, Jensen G, van der Smagt P. CNN-based segmentation of medical imaging data[EB/OL]. (2017-01-11)[2020-11-25]. https://arxiv.org/abs/1701.03056

    [51] Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C]. //2016 Fourth International Conference on 3D Vision (3DV), October 25-28, 2016, Stanford, CA, USA, 565-571(2016).

    [52] Feng X, Tustison N, Meyer C. Brain tumor segmentation using an ensemble of 3D U-nets and overall survival prediction using radiomic features[M]. //Crimi A, Bakas S, Kuijf H, et al. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 11384, 279-288(2019).

    [53] He C E, Xu H J, Wang Z et al. Automatic segmentation algorithm for multimodal magnetic resonance-based brain tumor images[J]. Acta Optica Sinica, 40, 0610001(2020).

    [54] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2014-09-04)[2020-11-25]. https://arxiv.org/abs/1409.1556

    [55] Pereira S, Pinto A, Alves V et al. Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI[M]. //Crimi A, Bakas S, Kuijf H, et al. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 9556, 131-143(2016).

    [56] Randhawa R S, Modi A, Jain P et al. Improving boundary classification for brain tumor segmentation and longitudinal disease progression[M]. //Crimi A, Bakas S, Kuijf H, et al. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 10154, 65-74(2016).

    [57] Hussain S, Anwar S M, Majid M. Brain tumor segmentation using cascaded deep convolutional neural network[C]. //2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 11-15, 2017, Jeju, Korea(South)., 1998-2001(2017).

    [58] Naceur M B, Saouli R, Akil M et al. Fully automatic brain tumor segmentation using end-to-end incremental deep neural networks in MRI images[J]. Computer Methods and Programs in Biomedicine, 166, 39-49(2018).

    [59] Mohsen H, El-Dahshan E S A, El-Horbaty E S M et al. Classification using deep learning neural networks for brain tumors[J]. Future Computing and Informatics Journal, 3, 68-71(2018).

    [60] Wang Y F, Wang L J, Wang H Y et al. End-to-end image super-resolution via deep and shallow convolutional networks[J]. IEEE Access, 7, 31959-31970(2019).

    [61] Zhao L Y, Jia K. Multiscale CNNs for brain tumor segmentation and diagnosis[J]. Computational and Mathematical Methods in Medicine, 2016, 8356294(2016).

    [62] Zhao L Y, Jia K B. Deep feature learning with discrimination mechanism for brain tumor segmentation and diagnosis[C]. //2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), September 23-25, 2015, Adelaide, SA, Australia., 306-309(2015).

    [63] Li J, Luo M, Luo X et al. Research on the application of brain tumor segmentation of MRI based on multi-scale convolutional neural networks[J]. China Medical Equipment, 13, 25-28(2016).

    [64] Bao S Q. Chung A C S. Multi-scale structured CNN with label consistency for brain MR image segmentation[J]. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 6, 113-117(2018).

    Zhiwei Li, Hui Cao, Feng Yang, Bin Cao. Research Progress of Brain Tumor Segmentation Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2400003
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