Ultrasound tissue classification: a review
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Ultrasound tissue classification: a review Caifeng Shan1 · Tao Tan2 · Jungong Han3 · Di Huang4
© Springer Nature B.V. 2020
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. Keywords Tissue classification · Tissue characterization · Machine learning · Deep learning · Ultrasound image analysis
1 Introduction Different medical imaging modalities are available and widely used nowadays in clinical practice to create images of the human body, such as computed tomography (CT), magnetic resonance imaging (MR), positron emission tomography (PET), and ultrasound (US). Among those, US imaging is the most widespread modality for visualizing human soft tissue, because of its advantages compared to others: cheap, harmless (no ionizing radiations), allowing real-time feedback, convenient to operate, well established technology present in all places, and so on. On the other hand, because of the limited field of view, shadows, speckle noise, and other artifacts in the US images, the interpretation of US images is sometimes difficult. * Caifeng Shan [email protected] 1
College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
2
Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
3
Department of Computer Science, Aberystwyth University, Penglais SY23 3DB, UK
4
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, China
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One main target of US image (signal) analysis is tissue classification. Ultrasound tissue classification is to analyze the characteristics of the US data and their correlation to the pathological state of tissue, and design a classifier to distinguish the US data into different tissue types (or states). Tissue classification in ultrasound has many important applications, such as cancer diagnosis (in prostate, breast, liver, etc.) and cardiovascular disease diagnosis and intervention. Driven by the unmet clinical needs to distinguish different tissue types (e.g., healthy versus diseased) in US, tissue characterization and classification have received much attention in recent years (Noble 2010; Thijss
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