Validation of the machine learning approach for 3D reconstruction of carotid artery from ultrasound imaging

It is important to investigate the state of the arteries in order to detect atherosclerotic plaques in the early stage and then treat them appropriately. One of the diagnostic techniques is the ultrasound (US) examination. In order to obtain a more detailed and comprehensive overview of the state of the patient's carotid artery, 3D reconstruction using the available 2D cross-sections can be performed. In this paper, deep learning is used for the automatic segmentation of US images, and this data is then used to reconstruct the 3D model of the patient-specific carotid artery. The validation of the proposed approach is performed by comparing two relevant clinical parameters for accessing the severity of vessel stenosis - the plaque length and the percentage of stenosis. Good validation results demonstrate that this method is capable of accurately performing segmentation of the lumen of carotid artery from US images and thus it can be a useful tool for assessing the state of the arteries in clinical diagnostics.