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Automatic cardiac evaluations using a deep video object segmentation network
BACKGROUND: Accurate cardiac volume and function assessment have valuable and significant diagnostic implications for patients suffering from ventricular dysfunction and cardiovascular disease. This study has focused on finding a reliable assistant to help physicians have more reliable and accurate...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994013/ https://www.ncbi.nlm.nih.gov/pubmed/35394221 http://dx.doi.org/10.1186/s13244-022-01212-9 |
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author | Sirjani, Nasim Moradi, Shakiba Oghli, Mostafa Ghelich Hosseinsabet, Ali Alizadehasl, Azin Yadollahi, Mona Shiri, Isaac Shabanzadeh, Ali |
author_facet | Sirjani, Nasim Moradi, Shakiba Oghli, Mostafa Ghelich Hosseinsabet, Ali Alizadehasl, Azin Yadollahi, Mona Shiri, Isaac Shabanzadeh, Ali |
author_sort | Sirjani, Nasim |
collection | PubMed |
description | BACKGROUND: Accurate cardiac volume and function assessment have valuable and significant diagnostic implications for patients suffering from ventricular dysfunction and cardiovascular disease. This study has focused on finding a reliable assistant to help physicians have more reliable and accurate cardiac measurements using a deep neural network. EchoRCNN is a semi-automated neural network for echocardiography sequence segmentation using a combination of mask region-based convolutional neural network image segmentation structure with reference-guided mask propagation video object segmentation network. RESULTS: The proposed method accurately segments the left and right ventricle regions in four-chamber view echocardiography series with a dice similarity coefficient of 94.03% and 94.97%, respectively. Further post-processing procedures on the segmented left and right ventricle regions resulted in a mean absolute error of 3.13% and 2.03% for ejection fraction and fractional area change parameters, respectively. CONCLUSION: This study has achieved excellent performance on the left and right ventricle segmentation, leading to more accurate estimations of vital cardiac parameters such as ejection fraction and fractional area change parameters in the left and right ventricle functionalities, respectively. The results represent that our method can predict an assured, accurate, and reliable cardiac function diagnosis in clinical screenings. |
format | Online Article Text |
id | pubmed-8994013 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-89940132022-04-22 Automatic cardiac evaluations using a deep video object segmentation network Sirjani, Nasim Moradi, Shakiba Oghli, Mostafa Ghelich Hosseinsabet, Ali Alizadehasl, Azin Yadollahi, Mona Shiri, Isaac Shabanzadeh, Ali Insights Imaging Original Article BACKGROUND: Accurate cardiac volume and function assessment have valuable and significant diagnostic implications for patients suffering from ventricular dysfunction and cardiovascular disease. This study has focused on finding a reliable assistant to help physicians have more reliable and accurate cardiac measurements using a deep neural network. EchoRCNN is a semi-automated neural network for echocardiography sequence segmentation using a combination of mask region-based convolutional neural network image segmentation structure with reference-guided mask propagation video object segmentation network. RESULTS: The proposed method accurately segments the left and right ventricle regions in four-chamber view echocardiography series with a dice similarity coefficient of 94.03% and 94.97%, respectively. Further post-processing procedures on the segmented left and right ventricle regions resulted in a mean absolute error of 3.13% and 2.03% for ejection fraction and fractional area change parameters, respectively. CONCLUSION: This study has achieved excellent performance on the left and right ventricle segmentation, leading to more accurate estimations of vital cardiac parameters such as ejection fraction and fractional area change parameters in the left and right ventricle functionalities, respectively. The results represent that our method can predict an assured, accurate, and reliable cardiac function diagnosis in clinical screenings. Springer Vienna 2022-04-08 /pmc/articles/PMC8994013/ /pubmed/35394221 http://dx.doi.org/10.1186/s13244-022-01212-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Sirjani, Nasim Moradi, Shakiba Oghli, Mostafa Ghelich Hosseinsabet, Ali Alizadehasl, Azin Yadollahi, Mona Shiri, Isaac Shabanzadeh, Ali Automatic cardiac evaluations using a deep video object segmentation network |
title | Automatic cardiac evaluations using a deep video object segmentation network |
title_full | Automatic cardiac evaluations using a deep video object segmentation network |
title_fullStr | Automatic cardiac evaluations using a deep video object segmentation network |
title_full_unstemmed | Automatic cardiac evaluations using a deep video object segmentation network |
title_short | Automatic cardiac evaluations using a deep video object segmentation network |
title_sort | automatic cardiac evaluations using a deep video object segmentation network |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8994013/ https://www.ncbi.nlm.nih.gov/pubmed/35394221 http://dx.doi.org/10.1186/s13244-022-01212-9 |
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