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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A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography
Ilias Gatos,Stavros Tsantis,Stavros Spiliopoulos,Dimitris Karnabatidis,I. Theotokas,Pavlos Zoumpoulis,Thanasis Loupas,John D. Hazle,George C. Kagadis,George C. Kagadis +9 more
TL;DR: New objective parameters and criteria for CLD diagnosis employing SWE images provided by the present study can be considered an important step toward color-based interpretation, and could assist radiologists' diagnostic performance on a daily basis after being installed in a PC and employed retrospectively, immediately after the examination.
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Learning-based prediction of gestational age from ultrasound images of the fetal brain
Ana I. L. Namburete,Richard V. Stebbing,Bryn Kemp,Mohammad Yaqub,Aris T. Papageorghiou,J. Alison Noble +5 more
TL;DR: In this paper, an automated framework for predicting gestational age and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance is proposed. But this method relies on a manifold surface representation of the fetal head which delineates the inner skull boundary and serves as a common coordinate system.
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Automatic apical view classification of echocardiograms using a discriminative learning dictionary.
TL;DR: A multi‐stage algorithm that employs spatio‐temporal feature extraction (Cuboid Detector) and supervised dictionary learning (LC‐KSVD) approaches to uniquely enhance the automatic recognition and classification accuracy of echocardiograms is presented.
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An efficient neural network based method for medical image segmentation
TL;DR: The proposed neural network based method segments X-ray computerized tomography (CT) and magnetic resonance (MR) head images much better than the incremental supervised neural network (ISNN) and SOM-based methods.
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Breast Lesions: Quantitative Diagnosis Using Ultrasound Shear Wave Elastography-A Systematic Review and Meta--Analysis.
Baoxian Liu,Yan-Ling Zheng,Guangliang Huang,Manxia Lin,Quan-Yuan Shan,Ying Lu,Wenshuo Tian,Xiaoyan Xie +7 more
TL;DR: SWE seems to be a good quantitative method for differentiating breast lesions, with promise for integration into routine imaging protocols, according to pooled weighted estimates of sensitivity and specificity.