Cargando…

CardioVinci: building blocks for virtual cardiac cells using deep learning

Advances in electron microscopy (EM) such as electron tomography and focused ion-beam scanning electron microscopy provide unprecedented, three-dimensional views of cardiac ultrastructures within sample volumes ranging from hundreds of nanometres to hundreds of micrometres. The datasets from these s...

Descripción completa

Detalles Bibliográficos
Autores principales: Khadangi, Afshin, Boudier, Thomas, Hanssen, Eric, Rajagopal, Vijay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9527637/
https://www.ncbi.nlm.nih.gov/pubmed/36189496
http://dx.doi.org/10.1098/rstb.2021.0469
_version_ 1784801117832478720
author Khadangi, Afshin
Boudier, Thomas
Hanssen, Eric
Rajagopal, Vijay
author_facet Khadangi, Afshin
Boudier, Thomas
Hanssen, Eric
Rajagopal, Vijay
author_sort Khadangi, Afshin
collection PubMed
description Advances in electron microscopy (EM) such as electron tomography and focused ion-beam scanning electron microscopy provide unprecedented, three-dimensional views of cardiac ultrastructures within sample volumes ranging from hundreds of nanometres to hundreds of micrometres. The datasets from these samples are typically large, with file sizes ranging from gigabytes to terabytes and the number of image slices within the three-dimensional stack in the hundreds. A significant bottleneck with these large datasets is the time taken to extract and statistically analyse three-dimensional changes in cardiac ultrastructures. This is because of the inherently low contrast and the significant amount of structural detail that is present in EM images. These datasets often require manual annotation, which needs substantial person-hours and may result in only partial segmentation that makes quantitative analysis of the three-dimensional volumes infeasible. We present CardioVinci, a deep learning workflow to automatically segment and statistically quantify the morphologies and spatial assembly of mitochondria, myofibrils and Z-discs with minimal manual annotation. The workflow encodes a probabilistic model of the three-dimensional cardiomyocyte using a generative adversarial network. This generative model can be used to create new models of cardiomyocyte architecture that reflect variations in morphologies and cell architecture found in EM datasets. This article is part of the theme issue ‘The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease’.
format Online
Article
Text
id pubmed-9527637
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher The Royal Society
record_format MEDLINE/PubMed
spelling pubmed-95276372022-10-11 CardioVinci: building blocks for virtual cardiac cells using deep learning Khadangi, Afshin Boudier, Thomas Hanssen, Eric Rajagopal, Vijay Philos Trans R Soc Lond B Biol Sci Articles Advances in electron microscopy (EM) such as electron tomography and focused ion-beam scanning electron microscopy provide unprecedented, three-dimensional views of cardiac ultrastructures within sample volumes ranging from hundreds of nanometres to hundreds of micrometres. The datasets from these samples are typically large, with file sizes ranging from gigabytes to terabytes and the number of image slices within the three-dimensional stack in the hundreds. A significant bottleneck with these large datasets is the time taken to extract and statistically analyse three-dimensional changes in cardiac ultrastructures. This is because of the inherently low contrast and the significant amount of structural detail that is present in EM images. These datasets often require manual annotation, which needs substantial person-hours and may result in only partial segmentation that makes quantitative analysis of the three-dimensional volumes infeasible. We present CardioVinci, a deep learning workflow to automatically segment and statistically quantify the morphologies and spatial assembly of mitochondria, myofibrils and Z-discs with minimal manual annotation. The workflow encodes a probabilistic model of the three-dimensional cardiomyocyte using a generative adversarial network. This generative model can be used to create new models of cardiomyocyte architecture that reflect variations in morphologies and cell architecture found in EM datasets. This article is part of the theme issue ‘The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease’. The Royal Society 2022-11-21 2022-10-03 /pmc/articles/PMC9527637/ /pubmed/36189496 http://dx.doi.org/10.1098/rstb.2021.0469 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Khadangi, Afshin
Boudier, Thomas
Hanssen, Eric
Rajagopal, Vijay
CardioVinci: building blocks for virtual cardiac cells using deep learning
title CardioVinci: building blocks for virtual cardiac cells using deep learning
title_full CardioVinci: building blocks for virtual cardiac cells using deep learning
title_fullStr CardioVinci: building blocks for virtual cardiac cells using deep learning
title_full_unstemmed CardioVinci: building blocks for virtual cardiac cells using deep learning
title_short CardioVinci: building blocks for virtual cardiac cells using deep learning
title_sort cardiovinci: building blocks for virtual cardiac cells using deep learning
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9527637/
https://www.ncbi.nlm.nih.gov/pubmed/36189496
http://dx.doi.org/10.1098/rstb.2021.0469
work_keys_str_mv AT khadangiafshin cardiovincibuildingblocksforvirtualcardiaccellsusingdeeplearning
AT boudierthomas cardiovincibuildingblocksforvirtualcardiaccellsusingdeeplearning
AT hansseneric cardiovincibuildingblocksforvirtualcardiaccellsusingdeeplearning
AT rajagopalvijay cardiovincibuildingblocksforvirtualcardiaccellsusingdeeplearning