Journal of Optoelectronics · Laser, Volume. 35, Issue 9, 1001(2024)

MRI segmentation of brain tumor based on the multi-task learning

CHAI Wenguang1, LI Wenhao1, and YAN Jingwen2
Author Affiliations
  • 1School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, Guangdong 510006, China
  • 2College of Engineering, Shantou University, Shantou, Guangdong 515063, China
  • show less
    References(20)

    [1] [1] PATEL A P, FISHER J L, NICHOLS E, et al. GBD 2016 brain and other CNS cancer collaborators. Global, regional, and national burden of brain and other CNS cancer, 1990—2016: A systematic analysis for the Global Burden of Disease Study 2016[J]. Lancet Neurology, 2019, 18(4): 376-393.

    [2] [2] CHEN F, WENDL M C, WYCZALKOWSKI M A, et al. Moving pan-cancer studies from basic research toward the clinic[J]. Nature Cancer, 2021, 2(9): 879-890.

    [4] [4] WONG K P. Medical image segmentation: methods and applications in functional imaging[M]//Handbook of Biomedical Image Analysis: Volume II: Segmentation Models Part B. Boston: Springer, 2005:111-182.

    [5] [5] CAHALL D E, RASOOL G, BOUAYNAYA N C, et al. Inception modules enhance brain tumor segmentation[J]. Frontiers in Computational Neuroscience, 2019, 13:44.

    [6] [6] MENZE B H, JAKAB A, BAUER S, et al. The multimodal brain tumor image segmentation benchmark (BRATS)[J]. IEEE Transactions on Medical Imaging, 2014, 34(10): 1993-2024.

    [7] [7] 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, October 5-9, 2015, Munich, Germany. Singapore: Springer, 2015:234-241.

    [8] [8] IEK , ABDULKADIR A, LIENKAMP S S, et al.3D U-Net: learning dense volumetric segmentation from sparse annotation[C]//International Conference on Medical Image Computing and Computer-assisted Intervention, October 17-21, 2016, Athens, Greece. Singapore: Springer, 2016:424-432.

    [9] [9] ISENSEE F, KICKINGEREDER P, WICK W, et al. No new-net[C]//International MICCAI Brainlesion Workshop, September 16, 2018, Granada, Spain. Singapore: Springer, 2018:234-244.

    [10] [10] BUKHARI S T, MOHY-UD-DIN H. E1D3 U-Net for brain tumor segmentation: submission to the RSNA-ASNR-MICCAI BraTS 2021 challenge[C]//International MICCAI Brainlesion Workshop, September 27, 2021, Virtual Event. Singapore: Springer, 2022:276-288.

    [11] [11] ISENSEE F, JAEGER P F, KOHL S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nature Methods, 2021, 18(2): 203-211.

    [12] [12] LEE S, PURUSHWALKAM S, COGSWELL M, et al. Why M heads are better than one: Training a diverse ensemble of deep networks[EB/OL].(2015-11-19)[2022-12-01]. https://arxiv.org/abs/1511.06314.

    [13] [13] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Seattle, Washington. New York: IEEE, 2016:2818-2826.

    [14] [14] LIU Z, MAO H, WU C Y, et al. A convnet for the 2020s[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-24, 2022, New Orleans, Louisiana. New York: IEEE, 2022:11976-11986.

    [15] [15] ULYANOV D, VEDALDI A, LEMPITSKY V. Instance normalization: The missing ingredient for fast stylization[EB/OL].(2017-11-6)[2022-12-01]. https://arxiv.org/abs/1607.08022.

    [16] [16] MAAS A L, HANNUN A Y, NG A Y. Rectifier nonlinearities improve neural network acoustic models[C]//the 30th International Conference on Machine Learning, June 16-21, 2013, Atlanta, Georgia. California: Stanford, 2013, 30(1): 3.

    [18] [18] 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, California, USA. New York: IEEE, 2016:565-571.

    [19] [19] MURPHY K P. Machine learning: a probabilistic perspective[M]. California: MIT Press, 2012:306.

    [20] [20] BAKAS S, REYES M, JAKAB A, et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge[EB/OL].(2019-04-23)[2022-12-01]. https://arxiv.org/abs/1811.02629.

    [22] [22] ZHANG D, HUANG G, ZHANG Q, et al. Cross-modality deep feature learning for brain tumor segmentation[J]. Pattern Recognition, 2021, 110:107562.

    [23] [23] HUA R, HUO Q, GAO Y, et al. Multimodal brain tumor segmentation using cascaded V-Nets[C]//International MICCAI Brainlesion Workshop, September 16, 2018, Granada, Spain. Singapore: Springer, 2018:49-60.

    Tools

    Get Citation

    Copy Citation Text

    CHAI Wenguang, LI Wenhao, YAN Jingwen. MRI segmentation of brain tumor based on the multi-task learning[J]. Journal of Optoelectronics · Laser, 2024, 35(9): 1001

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Dec. 21, 2022

    Accepted: Dec. 20, 2024

    Published Online: Dec. 20, 2024

    The Author Email:

    DOI:10.16136/j.joel.2024.09.0855

    Topics