Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Ono, Shunzaburo, Komatsu, Masaaki, Sakai, Akira, Arima, Hideki, Ochida, Mie, Aoyama, Rina, Yasutomi, Suguru, Asada, Ken, Kaneko, Syuzo, Sasano, Tetsuo, Hamamoto, Ryuji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784714672691216384
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
work_keys_str_mv AT onoshunzaburo automatedendocardialborderdetectionandleftventricularfunctionalassessmentinechocardiographyusingdeeplearning
AT komatsumasaaki automatedendocardialborderdetectionandleftventricularfunctionalassessmentinechocardiographyusingdeeplearning
AT sakaiakira automatedendocardialborderdetectionandleftventricularfunctionalassessmentinechocardiographyusingdeeplearning
AT arimahideki automatedendocardialborderdetectionandleftventricularfunctionalassessmentinechocardiographyusingdeeplearning
AT ochidamie automatedendocardialborderdetectionandleftventricularfunctionalassessmentinechocardiographyusingdeeplearning
AT aoyamarina automatedendocardialborderdetectionandleftventricularfunctionalassessmentinechocardiographyusingdeeplearning
AT yasutomisuguru automatedendocardialborderdetectionandleftventricularfunctionalassessmentinechocardiographyusingdeeplearning
AT asadaken automatedendocardialborderdetectionandleftventricularfunctionalassessmentinechocardiographyusingdeeplearning
AT kanekosyuzo automatedendocardialborderdetectionandleftventricularfunctionalassessmentinechocardiographyusingdeeplearning
AT sasanotetsuo automatedendocardialborderdetectionandleftventricularfunctionalassessmentinechocardiographyusingdeeplearning
AT hamamotoryuji automatedendocardialborderdetectionandleftventricularfunctionalassessmentinechocardiographyusingdeeplearning