• Acta Optica Sinica
  • Vol. 41, Issue 3, 0310002 (2021)
Hengliang Zhang, Qiang Li*, and Xin Guan
Author Affiliations
  • School of Microelectronics, Tianjin University, Tianjin 300072, China
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    DOI: 10.3788/AOS202141.0310002 Cite this Article Set citation alerts
    Hengliang Zhang, Qiang Li, Xin Guan. An Improved Three-Dimensional Dual-Path Brain Tumor Image Segmentation Network[J]. Acta Optica Sinica, 2021, 41(3): 0310002 Copy Citation Text show less
    References

    [1] Menze B H, Jakab A, Bauer S et al. The multimodal brain tumor image segmentation benchmark (BRATS)[J]. IEEE Transactions on Medical Imaging, 34, 1993-2024(2015).

    [2] Abd-Ellah M K, Awad A I, Khalaf A A M et al. A review on brain tumor diagnosis from MRI images: practical implications, key achievements, and lessons learned[J]. Magnetic Resonance Imaging, 61, 300-318(2019).

    [3] Li Q, Bai K X, Zhao L et al. Progresss and challenges of MRI brain tumor image segmentation[J]. Journal of Image and Graphics, 25, 419-431(2020).

    [4] Tong Y F, Li Q, Guan X. An improved multi-modal brain tumor segmentation hybrid algorithm[J]. Journal of Signal Processing, 34, 340-346(2018).

    [5] Ren L, Li Q, Guan X et al. Three-dimensional segmentation of brain tumors in magnetic resonance imaging based on improved continuous max-flow[J]. Laser & Optoelectronics Progress, 55, 111011(2018).

    [6] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. [C]∥Medical Image Computing and Computer-Assisted Intervention - MICCAI, 2015, 234-241(2015).

    [7] 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).

    [8] Çiçek Ö, Abdulkadir A, Lienkamp S S et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation. [C]∥Medical Image Computing and Computer-Assisted Intervention-MICCAI, 2016, 424-432(2016).

    [9] 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).

    [10] Xing B T, Li Q, Guan X. A brain tumor image segmentation method based on improved fully convolutional neural network[J]. Journal of Signal Processing, 34, 911-922(2018).

    [11] Chen C, Liu X P, Ding M et al. 3D dilated multi-fiber network for real-time brain tumor segmentation in MRI. [C]∥Medical Image Computing and Computer Assisted Intervention - MICCAI, 2019, 184-192(2019).

    [12] Nuechterlein N, Mehta S. 3D-ESPNet with pyramidal refinement for volumetric brain tumor image segmentation. [C]∥Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 245-253(2019).

    [13] Zhang XY, Zhou XY, Lin MX, et al.ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE Press, 2018: 6848- 6856.

    [14] Salehi S S M, Erdogmus D, Gholipour A. Tversky loss function for image segmentation using 3D fully convolutional deep networks. [C]∥Machine Learning in Medical Imaging, 379-387(2017).

    [15] Chen Y, Li J, Xiao H et al. Dual path networks. [C]∥Advances in Neural Information Processing Systems, 4467-4475(2017).

    [16] 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., 770-778(2016).

    [17] Huang G. Liu Z, van der Maaten L, et al. Densely connected convolutional networks[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2261-2269(2017).

    [18] 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).

    [19] 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]∥Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 128-141(2019).

    [20] Myronenko A[M]. 3D MRI brain tumor segmentation using autoencoder regularization, 311-320(2019).

    Hengliang Zhang, Qiang Li, Xin Guan. An Improved Three-Dimensional Dual-Path Brain Tumor Image Segmentation Network[J]. Acta Optica Sinica, 2021, 41(3): 0310002
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