Automatic localization of Common Carotid Artery in ultrasound images using Deep Learning

Hassanin, Dina; Abdellah, Mahmoud; Khalaf, Ashraf; Gharrieb, Redial Ragib

doi:10.21608/jaet.2020.41138.1040

Automatic localization of Common Carotid Artery in ultrasound images using Deep Learning

Public Titles

Authors

¹ Electrical Engineering Department, Egyptian Academy for Engineering and Advanced Technology, Egypt

² Electronics and Communications Department, Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt

³ Electrical Engineering Department, Faculty of Engineering, Minia University, Egypt

⁴ Electrical Engineering Department, Faculty of Engineering, Assiut University, Egypt

10.21608/jaet.2020.41138.1040

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.

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