A Deep Learning-Based Method for Vessel Wall Segmentation in 3D Carotid Ultrasound Images

Abstract

The invention discloses a three-dimensional carotid artery ultrasonic image blood vessel wall segmentation method based on deep learning. The method comprises the following steps: (1) obtaining a three-dimensional ultrasonic image; (2) obtaining a two-dimensional ultrasonic image of a carotid artery cross section, and performing manual marking; (3) dynamically and finely adjusting the convolutional neural network model by utilizing the manually marked image block; (4) fitting vascular adventitia-tunica media boundary initial contour; (5) using the dynamically adjusted convolutional neural network model to carry out vascular adventitia-tunica media boundary contour segmentation; (6) obtaining a vascular cavity ROI region; (7)using U-Net network to divide the vascular cavity, and extractingthe vascular cavity-tunica media boundary contour through morphological processing. According to the method, the contours of vascular adventitia-tunica media boundary MAB and vascular cavity-tunica media boundary LIB can be accurately segmented out; the workload of doctors is greatly reduced, and the vascular wall volume (VWV), the vascular wall thickness (VWT) and the vascular wall thickness change (VWT-Change) can be calculated based on the segmentation result of the method.

Description

technical field [0001] The invention belongs to the intersecting field of computer technology and medical images, and specifically relates to a deep learning-based method for segmenting blood vessel walls in three-dimensional carotid artery ultrasound images. Background technique [0002] In previous studies on ultrasound images of vascular plaques, most of them used two-dimensional B-ultrasound images. Intima-media thickness (IMT) is the most widely used index in clinical evaluation of plaque, which is calculated by calculating the media-adventitia boundary (MAB) and lumen-intima boundary (Lumen- intima Boundary, LIB) to get the distance between. In recent years, three-dimensional ultrasound has provided a more efficient, repeatable, and reliable means of detection and analysis of vascular plaques, which can more reliably analyze the composition, structure, and morphology of plaques while monitoring the effects of drug treatment. Effects of atherosclerosis. Taking the ca...

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Problems solved by technology

[0004] These carotid artery LIB and MAB segmentation methods mainly have two disadvantages: First, they require a lot of manual participation, which is very time-consuming and depends on the proficiency of the operator

Azzopardi et al. calculated the phase consistency map and input it to the CNN network to segment the MAB of the carotid artery, but this method is mainly used for two-dimensional ultrasound images, and can only segment the MAB

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