Detecting Carotid Intima-Media From Small-Sample Ultrasound Images

Shiyu Mi1; Zhanghong Wei2; Jinfeng Xu2; Zijun Yu1; Wenming Yang1; Qingmin Liao1

10.1109/EMBC44109.2020.9176282

2020

2375-7477

978-1-7281-1991-5

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

2129-2132

20-24 July 2020

Montreal, QC, Canada

Cardiovascular diseases are the biggest threat to human being's health all over the world, and carotid atherosclerotic plaque is the leading cause of ischemic cardiovascular diseases. To determine the location and shape of the plaque, it is of great significance to detect the intima-media (IM). In this paper, a new IM detection method based on convolution neural network (IMD-CNN) is proposed for the detection of IM of blood vessels in longitudinal ultrasonic images. In IMD-CNN, firstly the region of interest (ROI) is automatically extracted by morphological processing, then the patch-wise training data are constructed, and finally a simple CNN is trained to detect the IM. The experimental results obtained on 23 images show that the test accuracy of IMD-CNN is over 86% and the performance of IMD-CNN is also visually proved to be effective.

Biomedical imaging Blood vessels Training Ultrasonic imaging Atherosclerosis Data mining Media

其他

IEEE

1.Tsinghua University,Shenzhen Key Lab. of Info. Sci & Tech / Shenzhen Engineering Lab. of IS & DCP, Shenzhen International Graduate School,China
2.The First Affiliated Hospital of Southern University of Science and Technology,Department of Ultrasound,Shenzhen,China,518020

Shiyu Mi,Zhanghong Wei,Jinfeng Xu,et al. Detecting Carotid Intima-Media From Small-Sample Ultrasound Images[C],2020:2129-2132.