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Deep-Learning-Based Coronary Artery Calcium Detection from CT Image
One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-o...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588163/ https://www.ncbi.nlm.nih.gov/pubmed/34770366 http://dx.doi.org/10.3390/s21217059 |
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author | Lee, Sungjin Rim, Beanbonyka Jou, Sung-Shick Gil, Hyo-Wook Jia, Xibin Lee, Ahyoung Hong, Min |
author_facet | Lee, Sungjin Rim, Beanbonyka Jou, Sung-Shick Gil, Hyo-Wook Jia, Xibin Lee, Ahyoung Hong, Min |
author_sort | Lee, Sungjin |
collection | PubMed |
description | One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible. |
format | Online Article Text |
id | pubmed-8588163 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85881632021-11-13 Deep-Learning-Based Coronary Artery Calcium Detection from CT Image Lee, Sungjin Rim, Beanbonyka Jou, Sung-Shick Gil, Hyo-Wook Jia, Xibin Lee, Ahyoung Hong, Min Sensors (Basel) Article One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible. MDPI 2021-10-25 /pmc/articles/PMC8588163/ /pubmed/34770366 http://dx.doi.org/10.3390/s21217059 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lee, Sungjin Rim, Beanbonyka Jou, Sung-Shick Gil, Hyo-Wook Jia, Xibin Lee, Ahyoung Hong, Min Deep-Learning-Based Coronary Artery Calcium Detection from CT Image |
title | Deep-Learning-Based Coronary Artery Calcium Detection from CT Image |
title_full | Deep-Learning-Based Coronary Artery Calcium Detection from CT Image |
title_fullStr | Deep-Learning-Based Coronary Artery Calcium Detection from CT Image |
title_full_unstemmed | Deep-Learning-Based Coronary Artery Calcium Detection from CT Image |
title_short | Deep-Learning-Based Coronary Artery Calcium Detection from CT Image |
title_sort | deep-learning-based coronary artery calcium detection from ct image |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588163/ https://www.ncbi.nlm.nih.gov/pubmed/34770366 http://dx.doi.org/10.3390/s21217059 |
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