Optical Technique, Volume. 48, Issue 5, 634(2022)

MRI image segmentation method based on attention dilated U-Net

ZOU Lihua1 and JI Shanshan2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    References(8)

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    [8] [8] Duz O A, Saatci O, Karakulak E Z, et al. Olfactory dysfunction and cognition in radiologically isolated syndrome and relapsing-remitting multiple sclerosis[J]. European Neurology,2021,84(3):1-8.

    [14] [14] Cui H, Chang Y, Jiang L, et al. Multiscale attention guided U-Net architecture for cardiac segmentation in short-axis MRI images[J]. Computer Methods and Programs in Biomedicine,2021,206(1):106-122.

    [15] [15] Roy S, Butman J A, Reich D S, et al. Multiple sclerosis lesion segmentation from brain mri via fully convolutional neural networks[J]. Cell Signaling,2018,355(1):1124-1140.

    [16] [16] Essa E, Aldesouky D, Hussein S E, et al. Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation[J]. Medical & Biological Engineering & Computing,2020,58(5):2161-2175.

    [18] [18] Berk, Norman, Valentina, et al. Use of 2D U-Net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry[J]. Radiology,2018,288(1):177-185.

    [19] [19] Salem M, Valverde S, Cabezas M, et al. Multiple sclerosis lesion synthesis in MRI using an encoder-decoder U-NET[J]. IEEE Access,2019,7(1):25171-25184.

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    ZOU Lihua, JI Shanshan. MRI image segmentation method based on attention dilated U-Net[J]. Optical Technique, 2022, 48(5): 634

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    Paper Information

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    Received: Apr. 9, 2022

    Accepted: --

    Published Online: Jan. 20, 2023

    The Author Email:

    DOI:

    CSTR:32186.14.

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