SEGMENTATION OF CAROTID ARTERY FROM INTRAVASCULAR ULTRASOUND (IVUS) IMAGES USING DEEP LEARNING TECHNIQUES FOR PLAQUE IDENTIFICATION

Abstract

The carotid artery is the major artery that supplies blood to the brain, neck region, and face. The plaque deposition in these arteries is caused mainly due to the deposition of cholesterol, calcium, and other cellular debris carried along with the bloodstream. Hence identification of plaque is essential to avoid stroke and other diseases related to the heart. This paper proposes a deep learning-based segmentation algorithm for the identification of plaque in carotid artery using Intravascular Ultrasound (IVUS) images. To compare the performance of the proposed algorithm with the existing algorithms, evaluation metrics such as Jaccard Index (JI), Dice Similarity Coefficient (DC), and Hausdorff Distance (HD) are computed. From the results, it is observed that the proposed algorithm exhibited a high value with JI of 0.9562, DC of 0.9587, and HD of 4.8080.

Keywords

• intravascular ultrasound image
• segmentation
• deep learning
• jaccard index
• hausdorff distance