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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Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks
Christian F. Baumgartner,Konstantinos Kamnitsas,Jacqueline Matthew,Sandra Smith,Bernhard Kainz,Daniel Rueckert +5 more
TL;DR: A fully automated system based on convolutional neural networks which can detect twelve standard scan planes as defined by the UK fetal abnormality screening programme and can retrospectively retrieve correct scan planes with an accuracy of 71 % for cardiac views and 81 % for non-cardiac views is considered.