Machine learning for medical ultrasound: status, methods, and future opportunities.
Laura J. Brattain,Brian A. Telfer,Manish Dhyani,Manish Dhyani,Joseph R. Grajo,Anthony E. Samir +5 more
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TLDR
Leading machine learning approaches and research directions in US are reviewed, with an emphasis on recent ML advances, and an outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization is presented.Abstract:
Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.read more
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References
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Book ChapterDOI
Classification and Retrieval of Focal and Diffuse Liver from Ultrasound Images Using Machine Learning Techniques
TL;DR: This paper proposes a new method for the classification & retrieval of liver diseases from ultrasound image dataset that concentrates on diagnosing both focal and diffuse liver disease from ultrasound images.
Book ChapterDOI
Elastography: Applications and Limitations of a New Technology
TL;DR: Patients with indeterminate cytology are referred for diagnostic thyroid lobectomy and features associated with malignancy include microcalcifications, increased vascular flow, and irregular borders; however, no one feature or combination has been shown to reliably identify malignant nodules.
Journal ArticleDOI
The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images.
Hui Xiong,Laith R. Sultan,Theodore W. Cary,Susan M. Schultz,Ghizlane Bouzghar,Chandra M. Sehgal +5 more
TL;DR: The diagnostic performance, size measurements, and observer variability for automated leak-plugging segmentations were either comparable to or better than those of manual tracings.
Journal ArticleDOI
Lesion Type Classification by Applying Machine-Learning Technique to Contrast-Enhanced Ultrasound Images
TL;DR: 1D Haar-like features which describe intensity changes in a TIC and adopt the Adaboost machine learning technique, which eases understanding of which features are useful and gives statistical significance of differences between the proposed method and the conventional TIC analysis method using LDA.