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Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning
Knowledge about the anatomical structures of the left heart, specifically the atrium (LA) and ventricle (i.e., endocardium—Vendo—and epicardium—LVepi) is essential for the evaluation of cardiac functionality. Manual segmentation of cardiac structures from echocardiography is the baseline reference,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217142/ https://www.ncbi.nlm.nih.gov/pubmed/37238168 http://dx.doi.org/10.3390/diagnostics13101683 |
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author | Mortada, MHD Jafar Tomassini, Selene Anbar, Haidar Morettini, Micaela Burattini, Laura Sbrollini, Agnese |
author_facet | Mortada, MHD Jafar Tomassini, Selene Anbar, Haidar Morettini, Micaela Burattini, Laura Sbrollini, Agnese |
author_sort | Mortada, MHD Jafar |
collection | PubMed |
description | Knowledge about the anatomical structures of the left heart, specifically the atrium (LA) and ventricle (i.e., endocardium—Vendo—and epicardium—LVepi) is essential for the evaluation of cardiac functionality. Manual segmentation of cardiac structures from echocardiography is the baseline reference, but results are user-dependent and time-consuming. With the aim of supporting clinical practice, this paper presents a new deep-learning (DL)-based tool for segmenting anatomical structures of the left heart from echocardiographic images. Specifically, it was designed as a combination of two convolutional neural networks, the YOLOv7 algorithm and a U-Net, and it aims to automatically segment an echocardiographic image into LVendo, LVepi and LA. The DL-based tool was trained and tested on the Cardiac Acquisitions for Multi-Structure Ultrasound Segmentation (CAMUS) dataset of the University Hospital of St. Etienne, which consists of echocardiographic images from 450 patients. For each patient, apical two- and four-chamber views at end-systole and end-diastole were acquired and annotated by clinicians. Globally, our DL-based tool was able to segment LVendo, LVepi and LA, providing Dice similarity coefficients equal to 92.63%, 85.59%, and 87.57%, respectively. In conclusion, the presented DL-based tool proved to be reliable in automatically segmenting the anatomical structures of the left heart and supporting the cardiological clinical practice. |
format | Online Article Text |
id | pubmed-10217142 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102171422023-05-27 Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning Mortada, MHD Jafar Tomassini, Selene Anbar, Haidar Morettini, Micaela Burattini, Laura Sbrollini, Agnese Diagnostics (Basel) Article Knowledge about the anatomical structures of the left heart, specifically the atrium (LA) and ventricle (i.e., endocardium—Vendo—and epicardium—LVepi) is essential for the evaluation of cardiac functionality. Manual segmentation of cardiac structures from echocardiography is the baseline reference, but results are user-dependent and time-consuming. With the aim of supporting clinical practice, this paper presents a new deep-learning (DL)-based tool for segmenting anatomical structures of the left heart from echocardiographic images. Specifically, it was designed as a combination of two convolutional neural networks, the YOLOv7 algorithm and a U-Net, and it aims to automatically segment an echocardiographic image into LVendo, LVepi and LA. The DL-based tool was trained and tested on the Cardiac Acquisitions for Multi-Structure Ultrasound Segmentation (CAMUS) dataset of the University Hospital of St. Etienne, which consists of echocardiographic images from 450 patients. For each patient, apical two- and four-chamber views at end-systole and end-diastole were acquired and annotated by clinicians. Globally, our DL-based tool was able to segment LVendo, LVepi and LA, providing Dice similarity coefficients equal to 92.63%, 85.59%, and 87.57%, respectively. In conclusion, the presented DL-based tool proved to be reliable in automatically segmenting the anatomical structures of the left heart and supporting the cardiological clinical practice. MDPI 2023-05-09 /pmc/articles/PMC10217142/ /pubmed/37238168 http://dx.doi.org/10.3390/diagnostics13101683 Text en © 2023 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 Mortada, MHD Jafar Tomassini, Selene Anbar, Haidar Morettini, Micaela Burattini, Laura Sbrollini, Agnese Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning |
title | Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning |
title_full | Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning |
title_fullStr | Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning |
title_full_unstemmed | Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning |
title_short | Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning |
title_sort | segmentation of anatomical structures of the left heart from echocardiographic images using deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217142/ https://www.ncbi.nlm.nih.gov/pubmed/37238168 http://dx.doi.org/10.3390/diagnostics13101683 |
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