Machine learning for medical ultrasound: status, methods, and future opportunities

• Published in: Abdominal imaging Vol. 43; no. 4; pp. 786 - 799
• Main Authors: Brattain, Laura J., Telfer, Brian A., Dhyani, Manish, Grajo, Joseph R., Samir, Anthony E.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2018; Springer Nature B.V
• Subjects: Abdomen - diagnostic imaging; Artificial intelligence; Computation; Computer applications; Deep learning; Diagnostic systems; Forecasting; Gastroenterology; Hepatology; Humans; Image acquisition; Image enhancement; Image processing; Image quality; Imaging; Invited Article; Learning algorithms; Machine Learning; Medicine; Medicine & Public Health; Miniaturization; Quality control; Radiology; Ultrasonic imaging; Ultrasonic testing; Ultrasonography - methods; Ultrasound; Workflow; Deep learning; Sonography; Medical ultrasound; Elastography; Machine learning
• ISSN: 2366-004X; 2366-0058; 2366-0058
• DOI: 10.1007/s00261-018-1517-0

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.