A Novel Deep Learning Model for Medical Image Segmentation with Convolutional Neural Network and Transformer-Reference-Cited by-同舟云学术

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A Novel Deep Learning Model for Medical Image Segmentation with Convolutional Neural Network and Transformer

Published:2023-09-04 Issue:4 Volume:15 Page:663-677
ISSN:1913-2751
Container-title:Interdisciplinary Sciences: Computational Life Sciences
language:en
Short-container-title:Interdiscip Sci Comput Life Sci

Author:

Zhang Zhuo,Wu Hongbing,Zhao Huan,Shi Yicheng,Wang Jifang,Bai Hua,Sun Baoshan

Funder

National Natural Science Foundation of China

Tianjin Research Innovation Project for Postgraduate Students

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Computer Science Applications,General Biochemistry, Genetics and Molecular Biology

Link

https://link.springer.com/content/pdf/10.1007/s12539-023-00585-9.pdf

Reference46 articles.

1. Shirokikh B, Dalechina A, Shevtsov A et al (2020) Deep learning for brain tumor segmentation in radiosurgery: prospective clinical evaluation. In: LNIP, BrainLes 2019, vol 11992, Springer, Cham, pp 119–128. https://doi.org/10.1007/978-3-030-46640-4_12

2. Otsu N (2007) A threshold selection method from Gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66. https://doi.org/10.1109/TSMC.1979.4310076

3. Prastawa M, Bullitt E, Gerig G (2009) Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Med Image Anal 13(2):297–311. https://doi.org/10.1016/j.media.2008.11.002

4. Corso JJ, Sharon E, Dube S et al (2008) Efficient multilevel brain tumor segmentation with integrated Bayesian model classification. IEEE Trans Med Imaging 27(5):629–640. https://doi.org/10.1109/TMI.2007.912817

5. Lin AL, Chen BZ, Xu JY et al (2022) DS-TransUNet: dual swin transformer U-Net for medical image segmentation. IEEE Trans Instrum Meas 71:4005615. https://doi.org/10.1109/TIM.2022.3178991

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