Automatic Segmentation of Calcified Plaques and Vessel Borders from Abdominal Aorta CTA Images Using a 2D Spatial Convolutional Multilayer Machine Vision Classifier

Abstract

The aorta, including the thoracic and abdominal aortas, distributes blood from the heart to the upper and lower circulatory systems. Abdominal aortic sclerosis can cause abdominal aortic aneurysm. When the diameter of the aorta increases by more than 1.5 times its normal size (e.g., ≥4−5 cm, mean diameter in an adult is 2−3 cm), the risk of aneurysm rupture gradually increases. Aneurysm rupture and its associated bleeding have high mortality rates. Hence, the prediction of large diameter aortic aneurysms is an important issue with great clinical implications. In computed tomography angiography (CTA) imaging examination, an assistive tool for automatic image segmentation must be developed to help cardiologists and radiologists evaluate the risk of rupture progression and make appropriate treatment decisions. In this study, two‐dimensional (2D) spatial convolution operations (corner detection) and a multilayer machine vision classifier enabled the automatic segmentation of the luminal region, vascular intima, and calcified plaques in the abdominal aorta. The 2D spatial convolution operations can efficiently increase focus, remove unwanted noise in the convolutional layer, and segment calcified plaques and the vessel border by using a general regression neural network (GRNN)‐based classifier. The classifier's outputs can also be visualized in color presentation (red, green, and blue color series) to identify vessel borders and calcified plaques. Luminal change rate (LCR), calcified plaque rate (CPR), and ellipse ratio (ER) can be defined as criteria for evaluating changes in the luminal region and locating calcified plaques, which can be used to identify the locations of cross‐sectional shape changes and calcified plaques. Based on the collected CTA images from the clinical case studies, the proposed multilayer classifier has promising results for the intended medical purpose. With the LCR index, the correlation coefficients (R2) for manual image segmentation and automatic image segmentation is >0.90 in positive correlation for vessel borders segmentation. The proposed method is an adaptive learning scheme with potential clinical applications in automatic CTA image segmentation for further abdominal aortic aneurysm (AAA) risk evaluation. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.