Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks
| Autor: | Christopher B. Kendall, Jianming Liang, R. Todd Hurst, Jae Y. Shin, Nima Tajbakhsh |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer science Interface (computing) Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Machine learning computer.software_genre Convolutional neural network 030218 nuclear medicine & medical imaging Machine Learning (cs.LG) 03 medical and health sciences 0302 clinical medicine Region of interest 0202 electrical engineering electronic engineering information engineering cardiovascular diseases TRACE (psycholinguistics) business.industry Coronary artery calcium score Ultrasound Frame (networking) Computer Science - Learning Intima-media thickness cardiovascular system 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | CVPR |
| Popis: | Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations. J. Y. Shin, N. Tajbakhsh, R. T. Hurst, C. B. Kendall, and J. Liang. Automating carotid intima-media thickness video interpretation with convolutional neural networks. CVPR 2016, pp 2526-2535; N. Tajbakhsh, J. Y. Shin, R. T. Hurst, C. B. Kendall, and J. Liang. Automatic interpretation of CIMT videos using convolutional neural networks. Deep Learning for Medical Image Analysis, Academic Press, 2017 |
| Databáze: | OpenAIRE |
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