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Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking

The high rate of drug withdrawal from the market due to cardiovascular toxicity or lack of efficacy, the economic burden, and extremely long time before a compound reaches the market, have increased the relevance of human in vitro models like human (patient‐derived) pluripotent stem cell (hPSC)‐deri...

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Autores principales: Rivera‐Arbeláez, José M., Keekstra, Danjel, Cofiño‐Fabres, Carla, Boonen, Tom, Dostanic, Milica, ten Den, Simone A., Vermeul, Kim, Mastrangeli, Massimo, van den Berg, Albert, Segerink, Loes I., Ribeiro, Marcelo C., Strisciuglio, Nicola, Passier, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189437/
https://www.ncbi.nlm.nih.gov/pubmed/37206226
http://dx.doi.org/10.1002/btm2.10513
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author Rivera‐Arbeláez, José M.
Keekstra, Danjel
Cofiño‐Fabres, Carla
Boonen, Tom
Dostanic, Milica
ten Den, Simone A.
Vermeul, Kim
Mastrangeli, Massimo
van den Berg, Albert
Segerink, Loes I.
Ribeiro, Marcelo C.
Strisciuglio, Nicola
Passier, Robert
author_facet Rivera‐Arbeláez, José M.
Keekstra, Danjel
Cofiño‐Fabres, Carla
Boonen, Tom
Dostanic, Milica
ten Den, Simone A.
Vermeul, Kim
Mastrangeli, Massimo
van den Berg, Albert
Segerink, Loes I.
Ribeiro, Marcelo C.
Strisciuglio, Nicola
Passier, Robert
author_sort Rivera‐Arbeláez, José M.
collection PubMed
description The high rate of drug withdrawal from the market due to cardiovascular toxicity or lack of efficacy, the economic burden, and extremely long time before a compound reaches the market, have increased the relevance of human in vitro models like human (patient‐derived) pluripotent stem cell (hPSC)‐derived engineered heart tissues (EHTs) for the evaluation of the efficacy and toxicity of compounds at the early phase in the drug development pipeline. Consequently, the EHT contractile properties are highly relevant parameters for the analysis of cardiotoxicity, disease phenotype, and longitudinal measurements of cardiac function over time. In this study, we developed and validated the software HAARTA (Highly Accurate, Automatic and Robust Tracking Algorithm), which automatically analyzes contractile properties of EHTs by segmenting and tracking brightfield videos, using deep learning and template matching with sub‐pixel precision. We demonstrate the robustness, accuracy, and computational efficiency of the software by comparing it to the state‐of‐the‐art method (MUSCLEMOTION), and by testing it with a data set of EHTs from three different hPSC lines. HAARTA will facilitate standardized analysis of contractile properties of EHTs, which will be beneficial for in vitro drug screening and longitudinal measurements of cardiac function.
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spelling pubmed-101894372023-05-18 Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking Rivera‐Arbeláez, José M. Keekstra, Danjel Cofiño‐Fabres, Carla Boonen, Tom Dostanic, Milica ten Den, Simone A. Vermeul, Kim Mastrangeli, Massimo van den Berg, Albert Segerink, Loes I. Ribeiro, Marcelo C. Strisciuglio, Nicola Passier, Robert Bioeng Transl Med Research Articles The high rate of drug withdrawal from the market due to cardiovascular toxicity or lack of efficacy, the economic burden, and extremely long time before a compound reaches the market, have increased the relevance of human in vitro models like human (patient‐derived) pluripotent stem cell (hPSC)‐derived engineered heart tissues (EHTs) for the evaluation of the efficacy and toxicity of compounds at the early phase in the drug development pipeline. Consequently, the EHT contractile properties are highly relevant parameters for the analysis of cardiotoxicity, disease phenotype, and longitudinal measurements of cardiac function over time. In this study, we developed and validated the software HAARTA (Highly Accurate, Automatic and Robust Tracking Algorithm), which automatically analyzes contractile properties of EHTs by segmenting and tracking brightfield videos, using deep learning and template matching with sub‐pixel precision. We demonstrate the robustness, accuracy, and computational efficiency of the software by comparing it to the state‐of‐the‐art method (MUSCLEMOTION), and by testing it with a data set of EHTs from three different hPSC lines. HAARTA will facilitate standardized analysis of contractile properties of EHTs, which will be beneficial for in vitro drug screening and longitudinal measurements of cardiac function. John Wiley & Sons, Inc. 2023-04-18 /pmc/articles/PMC10189437/ /pubmed/37206226 http://dx.doi.org/10.1002/btm2.10513 Text en © 2023 The Authors. Bioengineering & Translational Medicine published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Rivera‐Arbeláez, José M.
Keekstra, Danjel
Cofiño‐Fabres, Carla
Boonen, Tom
Dostanic, Milica
ten Den, Simone A.
Vermeul, Kim
Mastrangeli, Massimo
van den Berg, Albert
Segerink, Loes I.
Ribeiro, Marcelo C.
Strisciuglio, Nicola
Passier, Robert
Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking
title Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking
title_full Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking
title_fullStr Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking
title_full_unstemmed Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking
title_short Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking
title_sort automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189437/
https://www.ncbi.nlm.nih.gov/pubmed/37206226
http://dx.doi.org/10.1002/btm2.10513
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