Dense cellular segmentation for EM using 2D–3D neural network ensembles

Abstract Biologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has been a major factor limiting insight into t...

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Autores principales:	Matthew D. Guay, Zeyad A. S. Emam, Adam B. Anderson, Maria A. Aronova, Irina D. Pokrovskaya, Brian Storrie, Richard D. Leapman
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/41216f2c37bc4441ab20307a221baaf2
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spelling	oai:doaj.org-article:41216f2c37bc4441ab20307a221baaf22021-12-02T13:23:50ZDense cellular segmentation for EM using 2D–3D neural network ensembles10.1038/s41598-021-81590-02045-2322https://doaj.org/article/41216f2c37bc4441ab20307a221baaf22021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81590-0https://doaj.org/toc/2045-2322Abstract Biologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has been a major factor limiting insight into the complex variability of cellular environments. Modern EM can produce gigavoxel image volumes containing large numbers of cells, but accurate manual segmentation of image features is slow and limits the creation of cell models. Segmentation algorithms based on convolutional neural networks can process large volumes quickly, but achieving EM task accuracy goals often challenges current techniques. Here, we define dense cellular segmentation as a multiclass semantic segmentation task for modeling cells and large numbers of their organelles, and give an example in human blood platelets. We present an algorithm using novel hybrid 2D–3D segmentation networks to produce dense cellular segmentations with accuracy levels that outperform baseline methods and approach those of human annotators. To our knowledge, this work represents the first published approach to automating the creation of cell models with this level of structural detail.Matthew D. GuayZeyad A. S. EmamAdam B. AndersonMaria A. AronovaIrina D. PokrovskayaBrian StorrieRichard D. LeapmanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matthew D. Guay Zeyad A. S. Emam Adam B. Anderson Maria A. Aronova Irina D. Pokrovskaya Brian Storrie Richard D. Leapman Dense cellular segmentation for EM using 2D–3D neural network ensembles
description	Abstract Biologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has been a major factor limiting insight into the complex variability of cellular environments. Modern EM can produce gigavoxel image volumes containing large numbers of cells, but accurate manual segmentation of image features is slow and limits the creation of cell models. Segmentation algorithms based on convolutional neural networks can process large volumes quickly, but achieving EM task accuracy goals often challenges current techniques. Here, we define dense cellular segmentation as a multiclass semantic segmentation task for modeling cells and large numbers of their organelles, and give an example in human blood platelets. We present an algorithm using novel hybrid 2D–3D segmentation networks to produce dense cellular segmentations with accuracy levels that outperform baseline methods and approach those of human annotators. To our knowledge, this work represents the first published approach to automating the creation of cell models with this level of structural detail.
format	article
author	Matthew D. Guay Zeyad A. S. Emam Adam B. Anderson Maria A. Aronova Irina D. Pokrovskaya Brian Storrie Richard D. Leapman
author_facet	Matthew D. Guay Zeyad A. S. Emam Adam B. Anderson Maria A. Aronova Irina D. Pokrovskaya Brian Storrie Richard D. Leapman
author_sort	Matthew D. Guay
title	Dense cellular segmentation for EM using 2D–3D neural network ensembles
title_short	Dense cellular segmentation for EM using 2D–3D neural network ensembles
title_full	Dense cellular segmentation for EM using 2D–3D neural network ensembles
title_fullStr	Dense cellular segmentation for EM using 2D–3D neural network ensembles
title_full_unstemmed	Dense cellular segmentation for EM using 2D–3D neural network ensembles
title_sort	dense cellular segmentation for em using 2d–3d neural network ensembles
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/41216f2c37bc4441ab20307a221baaf2
work_keys_str_mv	AT matthewdguay densecellularsegmentationforemusing2d3dneuralnetworkensembles AT zeyadasemam densecellularsegmentationforemusing2d3dneuralnetworkensembles AT adambanderson densecellularsegmentationforemusing2d3dneuralnetworkensembles AT mariaaaronova densecellularsegmentationforemusing2d3dneuralnetworkensembles AT irinadpokrovskaya densecellularsegmentationforemusing2d3dneuralnetworkensembles AT brianstorrie densecellularsegmentationforemusing2d3dneuralnetworkensembles AT richarddleapman densecellularsegmentationforemusing2d3dneuralnetworkensembles
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Dense cellular segmentation for EM using 2D–3D neural network ensembles

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