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Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images
Atherosclerotic carotid plaques have been shown to be closely associated with the risk of stroke. Since patients with symptomatic carotid plaques have a greater risk for stroke, stroke risk stratification based on the classification of carotid plaques into symptomatic or asymptomatic types is crucia...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9061007/ https://www.ncbi.nlm.nih.gov/pubmed/35509862 http://dx.doi.org/10.1155/2022/2014349 |
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author | Ma, Wei Xia, Yujiao Wu, Xiaoyan Yue, Zheng Cheng, Xinyao Fenster, Aaron Ding, Mingyue |
author_facet | Ma, Wei Xia, Yujiao Wu, Xiaoyan Yue, Zheng Cheng, Xinyao Fenster, Aaron Ding, Mingyue |
author_sort | Ma, Wei |
collection | PubMed |
description | Atherosclerotic carotid plaques have been shown to be closely associated with the risk of stroke. Since patients with symptomatic carotid plaques have a greater risk for stroke, stroke risk stratification based on the classification of carotid plaques into symptomatic or asymptomatic types is crucial in diagnosis, treatment planning, and medical treatment monitoring. A deep learning technique would be a good choice for implementing classification. Usually, to acquire a high-accuracy classification, a specific network architecture needs to be designed for a given classification task. In this study, we propose an object-specific four-path network (OSFP-Net) for stroke risk assessment by integrating ultrasound carotid plaques in both transverse and longitudinal sections of the bilateral carotid arteries. Each path of the OSFP-Net comprises of a feature extraction subnetwork (FE) and a feature downsampling subnetwork (FD). The FEs in the four paths use the same network structure to automatically extract features from ultrasound images of carotid plaques. The FDs use different object-specific pooling strategies for feature downsampling based on the observation that the sizes and shapes in the feature maps obtained from FEs should be different. The object-specific pooling strategies enable the network to accept arbitrarily sized carotid plaques as input and to capture a more informative context for improving the classification accuracy. Extensive experimental studies on a clinical dataset consisting of 333 subjects with 1332 carotid plaques show the superiority of our OSFP-Net against several state-of-the-art deep learning-based methods. The experimental results demonstrate better clinical agreement between the ground truth and the prediction, which indicates its great potential for use as a risk stratification and as a monitoring tool in the management of patients at risk for stroke. |
format | Online Article Text |
id | pubmed-9061007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-90610072022-05-03 Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images Ma, Wei Xia, Yujiao Wu, Xiaoyan Yue, Zheng Cheng, Xinyao Fenster, Aaron Ding, Mingyue Comput Math Methods Med Research Article Atherosclerotic carotid plaques have been shown to be closely associated with the risk of stroke. Since patients with symptomatic carotid plaques have a greater risk for stroke, stroke risk stratification based on the classification of carotid plaques into symptomatic or asymptomatic types is crucial in diagnosis, treatment planning, and medical treatment monitoring. A deep learning technique would be a good choice for implementing classification. Usually, to acquire a high-accuracy classification, a specific network architecture needs to be designed for a given classification task. In this study, we propose an object-specific four-path network (OSFP-Net) for stroke risk assessment by integrating ultrasound carotid plaques in both transverse and longitudinal sections of the bilateral carotid arteries. Each path of the OSFP-Net comprises of a feature extraction subnetwork (FE) and a feature downsampling subnetwork (FD). The FEs in the four paths use the same network structure to automatically extract features from ultrasound images of carotid plaques. The FDs use different object-specific pooling strategies for feature downsampling based on the observation that the sizes and shapes in the feature maps obtained from FEs should be different. The object-specific pooling strategies enable the network to accept arbitrarily sized carotid plaques as input and to capture a more informative context for improving the classification accuracy. Extensive experimental studies on a clinical dataset consisting of 333 subjects with 1332 carotid plaques show the superiority of our OSFP-Net against several state-of-the-art deep learning-based methods. The experimental results demonstrate better clinical agreement between the ground truth and the prediction, which indicates its great potential for use as a risk stratification and as a monitoring tool in the management of patients at risk for stroke. Hindawi 2022-04-25 /pmc/articles/PMC9061007/ /pubmed/35509862 http://dx.doi.org/10.1155/2022/2014349 Text en Copyright © 2022 Wei Ma et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Ma, Wei Xia, Yujiao Wu, Xiaoyan Yue, Zheng Cheng, Xinyao Fenster, Aaron Ding, Mingyue Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images |
title | Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images |
title_full | Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images |
title_fullStr | Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images |
title_full_unstemmed | Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images |
title_short | Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images |
title_sort | object-specific four-path network for stroke risk stratification of carotid arteries in ultrasound images |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9061007/ https://www.ncbi.nlm.nih.gov/pubmed/35509862 http://dx.doi.org/10.1155/2022/2014349 |
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