Deep-learning-based estimation of left ventricle myocardial strain from echocardiograms with occlusion artifacts.

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Journal of Medical Imaging Pub Date : 2025-09-01 Epub Date: 2025-09-27 DOI:10.1117/1.JMI.12.5.054002
Alan Romero-Pacheco, Nidiyare Hevia-Montiel, Blanca Vazquez, Fernando Arámbula Cosío, Jorge Perez-Gonzalez
{"title":"Deep-learning-based estimation of left ventricle myocardial strain from echocardiograms with occlusion artifacts.","authors":"Alan Romero-Pacheco, Nidiyare Hevia-Montiel, Blanca Vazquez, Fernando Arámbula Cosío, Jorge Perez-Gonzalez","doi":"10.1117/1.JMI.12.5.054002","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>We present a deep-learning-based methodology for estimating deformation in 2D echocardiograms. The goal is to automatically estimate the longitudinal strain of the left ventricle (LV) walls in images affected by speckle noise and acoustic occlusions.</p><p><strong>Approach: </strong>The proposed methodology integrates algorithms for converting sparse to dense flow, a Res-UNet architecture for automatic myocardium segmentation, flow estimation using a global motion aggregation network, and the computation of longitudinal strain curves and the global longitudinal strain (GLS) index. The approach was evaluated using two echocardiographic datasets in apical four-chamber view, both modified with noise and acoustic shadows. The CAMUS dataset ( <math><mrow><mi>N</mi> <mo>=</mo> <mn>250</mn></mrow> </math> ) was used for LV wall segmentation, whereas a synthetic image database ( <math><mrow><mi>N</mi> <mo>=</mo> <mn>2037</mn></mrow> </math> ) was employed for flow estimation.</p><p><strong>Results: </strong>Among the main performance metrics achieved are 98% [96 to 99] of correlation in the conversion from sparse to dense flow, a Dice index of <math><mrow><mn>88.2</mn> <mo>%</mo> <mo>±</mo> <mn>3.8</mn> <mo>%</mo></mrow> </math> for myocardial segmentation, an endpoint error of 0.133 [0.13 to 0.14] pixels in flow estimation, and an error of 1.34% [0.94 to 2.09] in the estimation of the GLS index.</p><p><strong>Conclusions: </strong>The results demonstrate improvements over previously reported performances while maintaining stability in echocardiograms with acoustic shadows. This methodology could be useful in clinical practice for the analysis of echocardiograms with noise artifacts and acoustic occlusions. Our code and trained models are publicly available at https://github.com/ArBioIIMAS/echo-gma.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 5","pages":"054002"},"PeriodicalIF":1.7000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476231/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1117/1.JMI.12.5.054002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/9/27 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose: We present a deep-learning-based methodology for estimating deformation in 2D echocardiograms. The goal is to automatically estimate the longitudinal strain of the left ventricle (LV) walls in images affected by speckle noise and acoustic occlusions.

Approach: The proposed methodology integrates algorithms for converting sparse to dense flow, a Res-UNet architecture for automatic myocardium segmentation, flow estimation using a global motion aggregation network, and the computation of longitudinal strain curves and the global longitudinal strain (GLS) index. The approach was evaluated using two echocardiographic datasets in apical four-chamber view, both modified with noise and acoustic shadows. The CAMUS dataset ( N = 250 ) was used for LV wall segmentation, whereas a synthetic image database ( N = 2037 ) was employed for flow estimation.

Results: Among the main performance metrics achieved are 98% [96 to 99] of correlation in the conversion from sparse to dense flow, a Dice index of 88.2 % ± 3.8 % for myocardial segmentation, an endpoint error of 0.133 [0.13 to 0.14] pixels in flow estimation, and an error of 1.34% [0.94 to 2.09] in the estimation of the GLS index.

Conclusions: The results demonstrate improvements over previously reported performances while maintaining stability in echocardiograms with acoustic shadows. This methodology could be useful in clinical practice for the analysis of echocardiograms with noise artifacts and acoustic occlusions. Our code and trained models are publicly available at https://github.com/ArBioIIMAS/echo-gma.

基于深度学习的超声心动图左心室心肌应变估计。
目的:我们提出了一种基于深度学习的方法来估计二维超声心动图的变形。目标是在受斑点噪声和声闭塞影响的图像中自动估计左心室(LV)壁的纵向应变。方法:该方法集成了将稀疏流转换为密集流的算法,用于自动心肌分割的Res-UNet架构,使用全局运动聚集网络的流量估计,以及纵向应变曲线和全局纵向应变(GLS)指数的计算。该方法使用两个超声心动图数据集在根尖四室视图中进行评估,都使用噪声和声学阴影进行修改。CAMUS数据集(N = 250)用于左室壁分割,而合成图像数据库(N = 2037)用于流量估计。结果:在实现的主要性能指标中,从稀疏到密集流转换的相关性为98%[96至99],心肌分割的Dice指数为89.2%±3.8%,流量估计的终点误差为0.133[0.13至0.14]像素,GLS指数估计的误差为1.34%[0.94至2.09]。结论:结果表明,在保持超声心动图稳定性的同时,比先前报道的性能有所改善。该方法可用于临床超声心动图噪声伪影和声学阻塞的分析。我们的代码和训练过的模型可以在https://github.com/ArBioIIMAS/echo-gma上公开获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Medical Imaging
Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.10
自引率
4.20%
发文量
0
期刊介绍: JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信