• Laser & Optoelectronics Progress
  • Vol. 56, Issue 10, 101001 (2019)
Jinghui Chu, Xiaochuan Li, Jiaqi Zhang, and Wei Lü*
Author Affiliations
  • School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  • show less
    DOI: 10.3788/LOP56.101001 Cite this Article Set citation alerts
    Jinghui Chu, Xiaochuan Li, Jiaqi Zhang, Wei Lü. Fine-Granted Segmentation Method for Three-Dimensional Brain Tumors Using Cascaded Convolutional Network[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101001 Copy Citation Text show less
    References

    [1] Mayer G S, Vrscay E R. Self-similarity of Fourier domain MRI data[J]. Nonlinear Analysis: Theory, Methods & Applications, 71, e855-e864(2009). http://www.sciencedirect.com/science/article/pii/S0362546X0800816X

    [2] Wolf D, Prankl J, Vincze M. Fast semantic segmentation of 3D point clouds using a dense CRF with learned parameters. [C]∥International Conference on Robotics and Automation (ICRA), May 26-30, 2015, Seattle, WA, USA. New York: IEEE, 4867-4873(2015).

    [3] Freedman D, Zhang T. Interactive graph cut based segmentation with shape priors. [C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), June 20-25, 2005, San Diego, CA, USA. New York: IEEE, 755-762(2005).

    [4] Ren L, Li Q, Guan X et al. 3D segmentation of brain tumors in MRI based on improved continuous max-flow[J]. Laser & Optoelectronics Progress, 55, 111011(2018).

    [5] Li R Z, Liu Y Y, Yang M et al. Three-dimensional point cloud segmentation algorithm based on improved region growing[J]. Laser & Optoelectronics Progress, 55, 051502(2018).

    [6] Xie Z N, Zheng D, Chen J Y et al. A tumor segmentation method of improved Chan-Vese model for liver cancer ablation computed tomography image[J]. Laser & Optoelectronics Progress, 54, 021702(2017).

    [7] Chu J H, Wang X Y, Lü W. 3D segmentation of breast MRI based on inter-frame correlations[J]. Journal of Tianjin University, 50, 835-842(2017).

    [8] Yao H B, Bian J W, Cong J W et al. Medical image segmentation model based on local sparse shape representation[J]. Laser & Optoelectronics Progress, 55, 051011(2018).

    [9] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017). http://ieeexplore.ieee.org/document/7478072/

    [10] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018). http://doi.ieeecomputersociety.org/10.1109/TPAMI.2017.2699184

    [11] Chen L C, Papandreou G, Kokkinos I, fully connected CRFs[EB/OL] et al. -06-07)[2018-06-30]. https:∥arxiv., org/abs/1412, 7062(2016).

    [12] Chen L C, Papandreou G, Schroff F et al. -12-05)[2018-06-30]. https:∥arxiv., org/abs/1706, 05587(2017).

    [13] Yu F. -04-30)[2018-06-30]. https:∥arxiv., org/abs/1511, 07122(2016).

    [14] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network. [C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 3690-3695(2017).

    [15] He K M, Zhang X Y, Ren S Q et al[M]. Spatial pyramid pooling in deep convolutional networks for visual recognition, 346-361(2014).

    [16] Guo C C, Yu F Q, Chen Y. Image semantic segmentation based on convolutional neural network feature and improved superpixel matching[J]. Laser & Optoelectronics Progress, 55, 081005(2018).

    [17] An Z, Xu X P, Yang J H et al. Design of augmented reality head-up display system based on image semantic segmentation[J]. Acta Optica Sinica, 38, 0710004(2018).

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

    [19] Ronneberger O, Fischer P, Brox T[M]. U-Net: convolutional networks for biomedical image segmentation, 234-241(2015).

    [20] Çiçek Ö, Abdulkadir A, Lienkamp S S et al[M]. 3D U-Net: learning dense volumetric segmentation from sparse annotation, 424-432(2016).

    [21] Szegedy C, Ioffe S, Vanhoucke V et al. -08-23)[2018-06-30]. https:∥arxiv., org/abs/1602, 07261(2016).

    [22] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

    [23] 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). http://www.ncbi.nlm.nih.gov/pubmed/27865153

    [24] Chen H, Dou Q, Yu L et al. -04-21)[2018-06-30]. https:∥arxiv., org/abs/1608, 05895(2016).

    [25] 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). http://europepmc.org/abstract/MED/26960222

    [26] Havaei M, Davy A, Warde-Farley D et al. Brain tumor segmentation with deep neural networks[J]. Medical Image Analysis, 35, 18-31(2017). http://www.ncbi.nlm.nih.gov/pubmed/27310171/

    [27] Cai J, Lu L, Xie Y, direct loss function[EB/OL] et al. -07-18)[2018-06-30]. https:∥arxiv., org/abs/1707, 04912(2017).

    [28] Menze B H, Jakab A, Bauer S et al. The multimodal brain tumor image segmentation benchmark (BRATS). [C]∥IEEE Transactions on Medical Imaging, December 4, 2014. New York: IEEE, 1993-2024(2015).

    [29] 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). http://www.nature.com/articles/sdata2017117

    [30] Bakas S, Akbari H, Sotiras A et al. Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection [2018-08-30]. https:∥doi.org/10.7937/K9/[2018-08-30]. TCIA., KLXWJJ1Q(2017).

    [31] Paszke A, Gross S, Chintala S et al[2018-08-30]. Automatic differentiation in PyTorch https:∥openreview.net/pdf?id=BJJsrmfCZ..

    [32] Kingma D P. -01-30)[2018-08-30]. https:∥arxiv., org/abs/1412, 6980(2017).

    Jinghui Chu, Xiaochuan Li, Jiaqi Zhang, Wei Lü. Fine-Granted Segmentation Method for Three-Dimensional Brain Tumors Using Cascaded Convolutional Network[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101001
    Download Citation