Development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms

Abstract Echocardiography is a widely used and clinically translatable imaging modality for the evaluation of cardiac structure and function in preclinical drug discovery and development. Echocardiograms are among the first in vivo diagnostic tools utilized to evaluate the heart due to its relativel...

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Autores principales:	Kristi Powers, Raymond Chang, Justin Torello, Rhonda Silva, Yannick Cadoret, William Cupelo, Lori Morton, Michael Dunn
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/60f29d1b81ba4614973066b5c9d3f9c0
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spelling	oai:doaj.org-article:60f29d1b81ba4614973066b5c9d3f9c02021-12-02T16:35:56ZDevelopment of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms10.1038/s41598-021-85971-32045-2322https://doaj.org/article/60f29d1b81ba4614973066b5c9d3f9c02021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85971-3https://doaj.org/toc/2045-2322Abstract Echocardiography is a widely used and clinically translatable imaging modality for the evaluation of cardiac structure and function in preclinical drug discovery and development. Echocardiograms are among the first in vivo diagnostic tools utilized to evaluate the heart due to its relatively low cost, high throughput acquisition, and non-invasive nature; however lengthy manual image analysis, intra- and inter-operator variability, and subjective image analysis presents a challenge for reproducible data generation in preclinical research. To combat the image-processing bottleneck and address both variability and reproducibly challenges, we developed a semi-automated analysis algorithm workflow to analyze long- and short-axis murine left ventricle (LV) ultrasound images. The long-axis B-mode algorithm executes a script protocol that is trained using a reference library of 322 manually segmented LV ultrasound images. The short-axis script was engineered to analyze M-mode ultrasound images in a semi-automated fashion using a pixel intensity evaluation approach, allowing analysts to place two seed-points to triangulate the local maxima of LV wall boundary annotations. Blinded operator evaluation of the semi-automated analysis tool was performed and compared to the current manual segmentation methodology for testing inter- and intra-operator reproducibility at baseline and after a pharmacologic challenge. Comparisons between manual and semi-automatic derivation of LV ejection fraction resulted in a relative difference of 1% for long-axis (B-mode) images and 2.7% for short-axis (M-mode) images. Our semi-automatic workflow approach reduces image analysis time and subjective bias, as well as decreases inter- and intra-operator variability, thereby enhancing throughput and improving data quality for pre-clinical in vivo studies that incorporate cardiac structure and function endpoints.Kristi PowersRaymond ChangJustin TorelloRhonda SilvaYannick CadoretWilliam CupeloLori MortonMichael DunnNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kristi Powers Raymond Chang Justin Torello Rhonda Silva Yannick Cadoret William Cupelo Lori Morton Michael Dunn Development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms
description	Abstract Echocardiography is a widely used and clinically translatable imaging modality for the evaluation of cardiac structure and function in preclinical drug discovery and development. Echocardiograms are among the first in vivo diagnostic tools utilized to evaluate the heart due to its relatively low cost, high throughput acquisition, and non-invasive nature; however lengthy manual image analysis, intra- and inter-operator variability, and subjective image analysis presents a challenge for reproducible data generation in preclinical research. To combat the image-processing bottleneck and address both variability and reproducibly challenges, we developed a semi-automated analysis algorithm workflow to analyze long- and short-axis murine left ventricle (LV) ultrasound images. The long-axis B-mode algorithm executes a script protocol that is trained using a reference library of 322 manually segmented LV ultrasound images. The short-axis script was engineered to analyze M-mode ultrasound images in a semi-automated fashion using a pixel intensity evaluation approach, allowing analysts to place two seed-points to triangulate the local maxima of LV wall boundary annotations. Blinded operator evaluation of the semi-automated analysis tool was performed and compared to the current manual segmentation methodology for testing inter- and intra-operator reproducibility at baseline and after a pharmacologic challenge. Comparisons between manual and semi-automatic derivation of LV ejection fraction resulted in a relative difference of 1% for long-axis (B-mode) images and 2.7% for short-axis (M-mode) images. Our semi-automatic workflow approach reduces image analysis time and subjective bias, as well as decreases inter- and intra-operator variability, thereby enhancing throughput and improving data quality for pre-clinical in vivo studies that incorporate cardiac structure and function endpoints.
format	article
author	Kristi Powers Raymond Chang Justin Torello Rhonda Silva Yannick Cadoret William Cupelo Lori Morton Michael Dunn
author_facet	Kristi Powers Raymond Chang Justin Torello Rhonda Silva Yannick Cadoret William Cupelo Lori Morton Michael Dunn
author_sort	Kristi Powers
title	Development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms
title_short	Development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms
title_full	Development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms
title_fullStr	Development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms
title_full_unstemmed	Development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms
title_sort	development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/60f29d1b81ba4614973066b5c9d3f9c0
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Development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms

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