Cardiovascular disorder (CVD) is one of the leading diseases which has a high mortality rate worldwide. Atherosclerosis is a condition which is a major cause of CVD and occurs due to the accumulation of plaque and calcium in the coronary arteries vessels. Intervascular ultrasound (IVUS) is a diagnostic technique that provides artery vessel imaging. To identify the severing of calcification and plaque in artery vessels, it is necessary to segment the IVUS imaging into lumen and media which demands specialized skills. For accurate diagnosis and prognosis of CVD in a patient, AI has provided many predictive algorithms which segment the IVUS imaging effectively. In this chapter, we will review the various deep-learning techniques exploited for IVUS imaging segmentation. We will also highlight the limitation of IVUS imaging that reduces the accuracy and effectiveness of CVD prediction.