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
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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Journal ArticleDOI
Ultrasound‐Based Machine Learning Approach for Detection of Nonalcoholic Fatty Liver Disease
Aylin Tahmasebi,Shuhao Wang,Corinne E. Wessner,Trang Vu,Ji-Bin Liu,Flemming Forsberg,Jesse Civan,Flavius F. Guglielmo,John R. Eisenbrey +8 more
TL;DR: In this paper , the authors explored the use of ultrasound with artificial intelligence for the detection of non-alcoholic fatty liver disease (NAFLD) in a prospective study and found that ultrasound can be used to detect NAFLD.
Patent
Systems and methods for automated ultrasound image labeling and quality grading
TL;DR: In this article, an ultrasound system includes an ultrasound imaging device configured to acquire ultrasound images of a patient, and an anatomical structure recognition and labeling module automatically labels the anatomical structures in the images with information that identifies anatomical structures.
Journal ArticleDOI
Passive Cavitation Imaging Artifact Reduction Using Data-Adaptive Spatial Filtering
TL;DR: In this article , a pixel-based mask was applied to DSI- or RCB-beamformed images to improve source localization and image quality without sacrificing computation time, and the difference in sensitivity, specificity, and area under the ROC curve (AUROC) differed by no more than 11% across all algorithms for both source densities and all source patterns.
Journal ArticleDOI
Segmentation and classification of renal tumors based on convolutional neural network
Zheng Gong,Liang Kan +1 more
TL;DR: In this article, the second most frequent urology tumors are the kidney tumors, which are of many types, mostly existing as malignant tumors, and the accuracy of segmentation and classification of kidney tumors is improved.
Journal ArticleDOI
Temporal contexts for motion tracking in ultrasound sequences with information bottleneck.
TL;DR: Wang et al. as mentioned in this paper proposed an online temporal adaptive convolutional neural network (TAdaCNN) to focus on feature extraction and enhance spatial features using temporal information, and information bottleneck was incorporated to achieve more accurate target tracking by maximally limiting the amount of information in the network and discarding irrelevant information.
References
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