Automatic localization of Common Carotid Artery in ultrasound images using Deep Learning | ||
| Journal of Advanced Engineering Trends | ||
| Public Titles, Volume 40, Issue 2, April 2021, Pages 127-135 PDF (978.6 K) | ||
| DOI: 10.21608/jaet.2020.41138.1040 | ||
| Authors | ||
| Dina Hassanin1; mahmoud Abdellah* 2; Ashraf Khalaf3; Redial Ragib Gharrieb4 | ||
| 1Electrical Engineering Department, Egyptian Academy for Engineering and Advanced Technology, Egypt | ||
| 2Electronics and Communications Department, Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt | ||
| 3Electrical Engineering Department, Faculty of Engineering, Minia University, Egypt | ||
| 4Electrical Engineering Department, Faculty of Engineering, Assiut University, Egypt | ||
| Abstract | ||
| Accurate and automatic localization of the common carotid artery (CCA) is extremely important because the narrowing of the CCA is a silent disease. CCA disease doesn't cause any symptoms in its early stages, and people don't realize that they usually have a problem until they have a stroke. A stroke occurs when the brain doesn't receive enough blood for a long time. Brain damage from a stroke can lead to loss of speech or vision, and major strokes can cause death. In this paper, we proposed a technique to localize the CCA in transverse section ultrasound (US) images using deep learning. First, we applied preprocessing to the images in the dataset before detecting the bounding box containing the CCA. We used a faster regional proposal convolutional neural network (Faster R-CNN) to detect the rectangular region(bounding box) around the CCA. Then we applied a circle localization technique to contour and localize the CCA in the US images. The proposed method has been performed on ultrasonic transverse images of the signal processing (SP) Lab. We compared our results with the clinicians' contours obtaining a great match between them. The accuracy of the bounding box detection was 97.5 and a Jaccard similarity of 90.86% between our proposed system and the clinicians' manual contours. Our proposed system has shown results that outperform other systems in Literature. | ||
| Keywords | ||
| Common Carotid Artery; Deep Learning; ultrasound images; Convolutional neural network; Localization of CCA | ||
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