Optical Technique, Volume. 49, Issue 1, 105(2023)

Liver segmentation network based on multilayer perceptron and multi-scale feature extraction

HU Yao, LI Jin, and WANG Yuanjun*
Author Affiliations
  • [in Chinese]
  • show less
    References(21)

    [4] [4] Lu XQ, Wu JS, Ren XY, et al. The study and application of the improved region growing algorithm for liver segmentation[J]. Optik,2014,125(9):2142-2147.

    [5] [5] 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,Munich,Germany:Springer,Cham,2015:234-241.

    [6] [6] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]∥Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition,Las Vegas,NV,USA:IEEE,2016:770-778.

    [7] [7] Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]∥Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition,Honolulu,HI,USA:IEEE,2017:4700-4708.

    [8] [8] Wang J, Lv P, Wang H, et al. SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in computed tomography[J]. Computer Methods and Programs in Biomedicine,2021,208:106268.

    [9] [9] Gu Z, Cheng J, Fu H, et al. CE-Net: Context encoder network for 2d medical image segmentation[J]. IEEE Transactions on Medical Imaging,2019,38(10):2281-2292.

    [10] [10] Chen J, Lu Y, Yu Q, et al. Transunet:Transformers make strong encoders for medical image segmentation[EB/OL].(2021-02-08)/ [2022-11-22].https:∥doi.org/10.48550/arXiv.2102.04306.

    [11] [11] Valanarasu J, Oza P, Hacihaliloglu I, et al. Medical transformer: Gated axial-attention for medical image segmentation[C]∥International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham:Springer,2021:36-46.

    [12] [12] Cao H, Wang Y, Chen J, et al. Swin-unet: Unet-like pure transformer for medical image segmentation[EB/OL]. (2021-05-12)/[2022-11-22].https:∥doi.org/10.48550/arXiv.2105.05537.

    [13] [13] Xu G P, Wu X R, Zhang X, et al. LeViT-UNet: make faster encoders with transformer for medical image segmentation[EB/OL]. (2021-07-19)/ [2022-11-22].https:∥arxiv.org/abs/2107.08623.2021.

    [14] [14] Bilic P, Christ P, Li H B, et al. The liver tumor segmentation benchmark (lits)[J/OL]. Medical Image Analysis. (2022-11-17)/ [2022-11-22].https:∥arxiv.org/abs/1901.04056.2019.

    [15] [15] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(8):2011-2023.

    [16] [16] Nasser A, Amr A, ElHabshy A A, et al. Efficient 3D deep learning model for medical image semantic segmentation[J]. Alexandria Engineering Journal,2021,60(1):1231-1239.

    [17] [17] Devlin J, Chang M W, Lee K, et al. Bert:Pre-training of deep bidirec-tional transformers for language understanding[EB/OL].(2019-05-24)/[2022-11-22].https:∥doi.org/10.48550/arXiv.1810.04805.

    [18] [18] Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[EB/OL]. (2021-06-13)/ [2022-11-22].https:∥doi.org/10.48550/arXiv.2010.11929.

    [19] [19] Valanarasu, J M J, Patel V M. UNeXt: MLP-based rapid medical image segmentation network[EB/OL]. (2022-03-09)/ [2022-11-22].https:∥doi.org/10.48550/arXiv.2203.04967.

    [20] [20] Chen L C, Papandreou G, Schroff F, et al. Rethinking atrous convolution for semantic image segmentation[EB/OL].(2017-12-05)/[2022-11-22].https:∥doi.org/10.48550/arXiv.1706.05587.

    [21] [21] Liu Y C, Shahid M, Sarapugdi W, et al. Cascaded atrous dual attention U-Net for tumor segmentation[J]. Multimedia Tools and Applications,2021,80(20):30007-30031.

    [22] [22] Fan T, Wang G, Wang X, et al. MSN-Net: A multi-scale context nested U-Net for liver segmentation[J]. Signal, Image and Video Processing,2021,15(6):1089-1097.

    [23] [23] Khan R A, Luo Y, Wu F X. RMS-UNet: Residual multi-scale UNet for liver and lesion segmentation[J]. Artificial Intelligence in Medicine,2022,124:102231.

    [24] [24] Mourya G K, Gogoi M, Talbar S N, et al. Cascaded dilated deep residual network for volumetric liver segmentation from CT image[J]. Nternational Journal of E-Health and Medical Communications,2021,12(1):34-45.

    Tools

    Get Citation

    Copy Citation Text

    HU Yao, LI Jin, WANG Yuanjun. Liver segmentation network based on multilayer perceptron and multi-scale feature extraction[J]. Optical Technique, 2023, 49(1): 105

    Download Citation

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

    Category:

    Received: Jun. 18, 2022

    Accepted: --

    Published Online: Mar. 19, 2023

    The Author Email: Yuanjun WANG (yjusst@126.com)

    DOI:

    CSTR:32186.14.

    Topics