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Ultrasound tissue classification: a review

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Abstract

Ultrasound imaging is the most widespread medical imaging modality for creating images of the human body in clinical practice. Tissue classification in ultrasound has been established as one of the most active research areas, driven by many important clinical applications. In this paper, we present a survey on ultrasound tissue classification, focusing on recent advances in this area. We start with a brief review on the main clinical applications. We then introduce the traditional approaches, where the existing research on feature extraction and classifier design are reviewed. As deep learning approaches becoming popular for medical image analysis, the recent deep learning methods for tissue classification are also introduced. We briefly discuss the FDA-cleared techniques being used clinically. We conclude with the discussion on the challenges and research focus in future.

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  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Caifeng Shan

  2. Eindhoven University of Technology, Eindhoven, 5600 MB, The Netherlands

    Tao Tan

  3. Department of Computer Science, Aberystwyth University, Penglais, SY23 3DB, UK

    Jungong Han

  4. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China

    Di Huang

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Shan, C., Tan, T., Han, J. et al. Ultrasound tissue classification: a review. Artif Intell Rev 54, 3055–3088 (2021). https://doi.org/10.1007/s10462-020-09920-8

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