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Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning
Endocardial border detection is a key step in assessing left ventricular systolic function in echocardiography. However, this process is still not sufficiently accurate, and manual retracing is often required, causing time-consuming and intra-/inter-observer variability in clinical practice. To addr...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138644/ https://www.ncbi.nlm.nih.gov/pubmed/35625819 http://dx.doi.org/10.3390/biomedicines10051082 |
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author | Ono, Shunzaburo Komatsu, Masaaki Sakai, Akira Arima, Hideki Ochida, Mie Aoyama, Rina Yasutomi, Suguru Asada, Ken Kaneko, Syuzo Sasano, Tetsuo Hamamoto, Ryuji |
author_facet | Ono, Shunzaburo Komatsu, Masaaki Sakai, Akira Arima, Hideki Ochida, Mie Aoyama, Rina Yasutomi, Suguru Asada, Ken Kaneko, Syuzo Sasano, Tetsuo Hamamoto, Ryuji |
author_sort | Ono, Shunzaburo |
collection | PubMed |
description | Endocardial border detection is a key step in assessing left ventricular systolic function in echocardiography. However, this process is still not sufficiently accurate, and manual retracing is often required, causing time-consuming and intra-/inter-observer variability in clinical practice. To address these clinical issues, more accurate and normalized automatic endocardial border detection would be valuable. Here, we develop a deep learning-based method for automated endocardial border detection and left ventricular functional assessment in two-dimensional echocardiographic videos. First, segmentation of the left ventricular cavity was performed in the six representative projections for a cardiac cycle. We employed four segmentation methods: U-Net, UNet++, UNet3+, and Deep Residual U-Net. UNet++ and UNet3+ showed a sufficiently high performance in the mean value of intersection over union and Dice coefficient. The accuracy of the four segmentation methods was then evaluated by calculating the mean value for the estimation error of the echocardiographic indexes. UNet++ was superior to the other segmentation methods, with the acceptable mean estimation error of the left ventricular ejection fraction of 10.8%, global longitudinal strain of 8.5%, and global circumferential strain of 5.8%, respectively. Our method using UNet++ demonstrated the best performance. This method may potentially support examiners and improve the workflow in echocardiography. |
format | Online Article Text |
id | pubmed-9138644 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91386442022-05-28 Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning Ono, Shunzaburo Komatsu, Masaaki Sakai, Akira Arima, Hideki Ochida, Mie Aoyama, Rina Yasutomi, Suguru Asada, Ken Kaneko, Syuzo Sasano, Tetsuo Hamamoto, Ryuji Biomedicines Article Endocardial border detection is a key step in assessing left ventricular systolic function in echocardiography. However, this process is still not sufficiently accurate, and manual retracing is often required, causing time-consuming and intra-/inter-observer variability in clinical practice. To address these clinical issues, more accurate and normalized automatic endocardial border detection would be valuable. Here, we develop a deep learning-based method for automated endocardial border detection and left ventricular functional assessment in two-dimensional echocardiographic videos. First, segmentation of the left ventricular cavity was performed in the six representative projections for a cardiac cycle. We employed four segmentation methods: U-Net, UNet++, UNet3+, and Deep Residual U-Net. UNet++ and UNet3+ showed a sufficiently high performance in the mean value of intersection over union and Dice coefficient. The accuracy of the four segmentation methods was then evaluated by calculating the mean value for the estimation error of the echocardiographic indexes. UNet++ was superior to the other segmentation methods, with the acceptable mean estimation error of the left ventricular ejection fraction of 10.8%, global longitudinal strain of 8.5%, and global circumferential strain of 5.8%, respectively. Our method using UNet++ demonstrated the best performance. This method may potentially support examiners and improve the workflow in echocardiography. MDPI 2022-05-06 /pmc/articles/PMC9138644/ /pubmed/35625819 http://dx.doi.org/10.3390/biomedicines10051082 Text en © 2022 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 Ono, Shunzaburo Komatsu, Masaaki Sakai, Akira Arima, Hideki Ochida, Mie Aoyama, Rina Yasutomi, Suguru Asada, Ken Kaneko, Syuzo Sasano, Tetsuo Hamamoto, Ryuji Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning |
title | Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning |
title_full | Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning |
title_fullStr | Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning |
title_full_unstemmed | Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning |
title_short | Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning |
title_sort | automated endocardial border detection and left ventricular functional assessment in echocardiography using deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138644/ https://www.ncbi.nlm.nih.gov/pubmed/35625819 http://dx.doi.org/10.3390/biomedicines10051082 |
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