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A Review of Deep-Learning-Based Medical Image Segmentation Methods

Author

Listed:
  • Xiangbin Liu

    (Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410000, China
    College of Information Science and Engineering, Hunan Normal University, Changsha 410000, China
    Xiangjiang Institute of Artificial Intelligence, Changsha 410000, China)

  • Liping Song

    (Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410000, China
    College of Information Science and Engineering, Hunan Normal University, Changsha 410000, China
    Xiangjiang Institute of Artificial Intelligence, Changsha 410000, China)

  • Shuai Liu

    (Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410000, China
    College of Information Science and Engineering, Hunan Normal University, Changsha 410000, China
    Xiangjiang Institute of Artificial Intelligence, Changsha 410000, China)

  • Yudong Zhang

    (School of Informatics, University of Leicester, Leicester LE1 7RH, UK)

Abstract

As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.

Suggested Citation

  • Xiangbin Liu & Liping Song & Shuai Liu & Yudong Zhang, 2021. "A Review of Deep-Learning-Based Medical Image Segmentation Methods," Sustainability, MDPI, vol. 13(3), pages 1-29, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1224-:d:486444
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    References listed on IDEAS

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    1. Zhen Ma & João Manuel R.S. Tavares & Renato Natal Jorge & T. Mascarenhas, 2010. "A review of algorithms for medical image segmentation and their applications to the female pelvic cavity," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 13(2), pages 235-246.
    2. Ana Ferreira & Fernanda Gentil & João Manuel R. S. Tavares, 2014. "Segmentation algorithms for ear image data towards biomechanical studies," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 17(8), pages 888-904, June.
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    Cited by:

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    2. Navin Ranjan & Sovit Bhandari & Pervez Khan & Youn-Sik Hong & Hoon Kim, 2021. "Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder," Sustainability, MDPI, vol. 13(9), pages 1-26, May.
    3. Jiang, Man & Yang, Siluo & Gao, Qiang, 2024. "Multidimensional indicators to identify emerging technologies: Perspective of technological knowledge flow," Journal of Informetrics, Elsevier, vol. 18(1).

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