Machine learning for medical ultrasound: status, methods, and future opportunities.
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 opportun...
| Published in: | Abdominal Radiology Vol. 43; no. 4; pp. 786 - 800 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Apr2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=128888776&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128888776 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Apr2018 vid: 43 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 128888776 10.1007/s00261-018-1517-0 128888776 ppf: 786 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning for medical ultrasound: status, methods, and future opportunities. aug: au: Brattain, Laura J. Telfer, Brian A. Dhyani, Manish Grajo, Joseph R. Samir, Anthony E. affil: MIT Lincoln Laboratory, 244 Wood St, 02420, Lexington, MA, USA sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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