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
Citations
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Proceedings ArticleDOI
Learning Local Feature Descriptions in 3D Ultrasound
TL;DR: In this article, a 3D convolutional autoencoder (AE) with a custom loss function is used to encode 3DUS image patches into a compact latent space that serves as a general feature description.
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Deep-Learning High-Dynamic-Range Ultrasound
TL;DR: It is demonstrated that this type of networks can be trained to predict HDR out from a minimal number of input expositions, while the obtained results showed to be comparable with more traditional approaches.
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Ultrasound Detection of Subquadricipital Recess Distension
Marco Colussi,Gabriele Civitarese,Dragan Ahmetovic,Claudio Bettini,Roberta Gualtierotti,Flora Peyvandi,Sergio Mascetti +6 more
TL;DR: In this paper , the problem of automatically detecting the recess and assessing whether it is distended in knee ultrasound images collected in patients with hemophilia was addressed by adopting a one-stage object detection algorithm, while the second one is a multi-task approach with a classification and a detection branch.
Journal ArticleDOI
Machine learning for accurate estimation of fetal gestational age based on ultrasound images
Lok Hin Lee,Elizabeth Bradburn,Rachel Craik,Mohammad Yaqub,Shane A. Norris,Leila Cheikh Ismail,Eric O Ohuma,Fernando C. Barros,A Lambert,Maria J. Carvalho,Y A Jaffer,Michael Gravett,Manorama Purwar,Qingqing Wu,Enrico Bertini,Shama Munim,Aung Myat Min,Zulfiqar A Bhutta,José Villar,Stephen Kennedy,J. Alison Noble,Aris T. Papageorghiou +21 more
TL;DR: In this paper , the authors used machine learning methods to estimate gestational age using only image analysis of standard ultrasound planes, without any measurement information, and showed that this approach compensates for increases in size variation and is even accurate in cases of intrauterine growth restriction.
Journal ArticleDOI
AI-based Ultrasound Imaging Technologies for Hepatic Diseases
TL;DR: In this article , a review summarizes the current rapid development of US technology and related AI methods in the diagnosis and treatment of hepatic diseases, including steatosis grading, fibrosis staging, detection of focal liver lesions, US image segmentation, multimodal image registration, and other applications.
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