The automatic segmentation model of CT angiography based on ResUNet and PSPNet assists in the diagnosis of carotid atherosclerotic plaques with accurate precision
GUO Zhongping
GU Yan
Abstract：Objective To explore the value of the automatic segmentation model of computed tomography angiography(CTA)based on ResUNet and PSPNet in helping to evaluate carotid atherosclerotic plaques.Methods This study retrospectively included 647 patients with carotid atherosclerotic plaque formation who underwent head and neck CTA examinations.They were randomly divided into training set(n=475),validation set(n=86)and test set(n=86)at a ratio of 7:1.5:1.5.The images marked by radiologists in the training set were used to develop the automatic segmentation model based on ResUNet and PSPNet.Parameters such as precision,sensitivity,and recall were used in the validation set and the test set to evaluate the diagnostic performance of the model for carotid plaques.Results In the training set,the automatic segmentation model had already demonstrated good performance in the segmentation of atherosclerotic plaques.Its practicability was further verified in the validation set and the test set.In addition,a subgroup analysis of different plaque types was conducted on the test set,and the results showed that the deep learning model based on CTA images demonstrated good plaque diagnostic accuracy for different plaque types.Conclusion The automatic segmentation model based on ResUNet and PSPNet has relatively high accuracy in assisting the diagnosis of carotid atherosclerotic plaques and is clinically feasible.
Keywords：carotid plaquedeep learningstroke
Publication Date：2025-04-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 479-483 )