An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction
Main Author: | |
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Publication Date: | 2022 |
Other Authors: | , , , , , , , , , , |
Format: | Article |
Language: | eng |
Source: | Repositório Institucional da UNESP |
Download full: | http://dx.doi.org/10.3390/app12073531 http://hdl.handle.net/11449/241734 |
Summary: | Despite the use of new techniques on embryo selection and the presence of equipment on the market, such as EmbryoScope® and Geri®, which help in the evaluation of embryo quality, there is still a subjectivity between the embryologist’s classifications, which are subjected to interand intra-observer variability, therefore compromising the successful implantation of the embryo. Nonetheless, with the acquisition of images through the time-lapse system, it is possible to perform digital processing of these images, providing a better analysis of the embryo, in addition to enabling the automatic analysis of a large volume of information. An image processing protocol was developed using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each one representing a different aspect of the embryo and describing a different characteristic of the blastocyst. These variables can be categorized into texture, gray-level average, gray-level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. The image processing protocol can segment human blastocyst images and automatically extract 33 variables that describe quantitative aspects of the blastocyst’s regions, with potential utility in embryo selection for assisted reproductive technology (ART). |
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An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproductionblastocystdigital image processingembryo selectionmorphology-derived variablessegmentationDespite the use of new techniques on embryo selection and the presence of equipment on the market, such as EmbryoScope® and Geri®, which help in the evaluation of embryo quality, there is still a subjectivity between the embryologist’s classifications, which are subjected to interand intra-observer variability, therefore compromising the successful implantation of the embryo. Nonetheless, with the acquisition of images through the time-lapse system, it is possible to perform digital processing of these images, providing a better analysis of the embryo, in addition to enabling the automatic analysis of a large volume of information. An image processing protocol was developed using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each one representing a different aspect of the embryo and describing a different characteristic of the blastocyst. These variables can be categorized into texture, gray-level average, gray-level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. The image processing protocol can segment human blastocyst images and automatically extract 33 variables that describe quantitative aspects of the blastocyst’s regions, with potential utility in embryo selection for assisted reproductive technology (ART).Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Laboratory of Applied Mathematics Department of Biological Sciences São Paulo State University (UNESP)Graduate Program in Pharmacology and Biotechnology Institute of Biosciences São Paulo State University (UNESP)Laboratory of Embryonic Micromanipulation Department of Biological Sciences São Paulo State University (UNESP)IVF Laboratory IVI ValenciaHealth Research Institute La FeLaboratory of Applied Mathematics Department of Biological Sciences São Paulo State University (UNESP)Graduate Program in Pharmacology and Biotechnology Institute of Biosciences São Paulo State University (UNESP)Laboratory of Embryonic Micromanipulation Department of Biological Sciences São Paulo State University (UNESP)CAPES: 001Universidade Estadual Paulista (UNESP)IVI ValenciaHealth Research Institute La FeChéles, Dóris Spinosa [UNESP]Ferreira, André Satoshi [UNESP]de Jesus, Isabela Sueitt [UNESP]Fernandez, Eleonora Inácio [UNESP]Pinheiro, Gabriel Martins [UNESP]Dal Molin, Eloiza Adriane [UNESP]Alves, Wallace [UNESP]de Souza, Rebeca Colauto Milanezi [UNESP]Bori, LorenaMeseguer, MarcosRocha, José Celso [UNESP]Nogueira, Marcelo Fábio Gouveia [UNESP]2023-03-01T21:19:02Z2023-03-01T21:19:02Z2022-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/app12073531Applied Sciences (Switzerland), v. 12, n. 7, 2022.2076-3417http://hdl.handle.net/11449/24173410.3390/app120735312-s2.0-85128219618Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengApplied Sciences (Switzerland)info:eu-repo/semantics/openAccess2024-06-13T17:38:41Zoai:repositorio.unesp.br:11449/241734Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:21:01.579382Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction |
title |
An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction |
spellingShingle |
An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction Chéles, Dóris Spinosa [UNESP] blastocyst digital image processing embryo selection morphology-derived variables segmentation |
title_short |
An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction |
title_full |
An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction |
title_fullStr |
An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction |
title_full_unstemmed |
An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction |
title_sort |
An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction |
author |
Chéles, Dóris Spinosa [UNESP] |
author_facet |
Chéles, Dóris Spinosa [UNESP] Ferreira, André Satoshi [UNESP] de Jesus, Isabela Sueitt [UNESP] Fernandez, Eleonora Inácio [UNESP] Pinheiro, Gabriel Martins [UNESP] Dal Molin, Eloiza Adriane [UNESP] Alves, Wallace [UNESP] de Souza, Rebeca Colauto Milanezi [UNESP] Bori, Lorena Meseguer, Marcos Rocha, José Celso [UNESP] Nogueira, Marcelo Fábio Gouveia [UNESP] |
author_role |
author |
author2 |
Ferreira, André Satoshi [UNESP] de Jesus, Isabela Sueitt [UNESP] Fernandez, Eleonora Inácio [UNESP] Pinheiro, Gabriel Martins [UNESP] Dal Molin, Eloiza Adriane [UNESP] Alves, Wallace [UNESP] de Souza, Rebeca Colauto Milanezi [UNESP] Bori, Lorena Meseguer, Marcos Rocha, José Celso [UNESP] Nogueira, Marcelo Fábio Gouveia [UNESP] |
author2_role |
author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) IVI Valencia Health Research Institute La Fe |
dc.contributor.author.fl_str_mv |
Chéles, Dóris Spinosa [UNESP] Ferreira, André Satoshi [UNESP] de Jesus, Isabela Sueitt [UNESP] Fernandez, Eleonora Inácio [UNESP] Pinheiro, Gabriel Martins [UNESP] Dal Molin, Eloiza Adriane [UNESP] Alves, Wallace [UNESP] de Souza, Rebeca Colauto Milanezi [UNESP] Bori, Lorena Meseguer, Marcos Rocha, José Celso [UNESP] Nogueira, Marcelo Fábio Gouveia [UNESP] |
dc.subject.por.fl_str_mv |
blastocyst digital image processing embryo selection morphology-derived variables segmentation |
topic |
blastocyst digital image processing embryo selection morphology-derived variables segmentation |
description |
Despite the use of new techniques on embryo selection and the presence of equipment on the market, such as EmbryoScope® and Geri®, which help in the evaluation of embryo quality, there is still a subjectivity between the embryologist’s classifications, which are subjected to interand intra-observer variability, therefore compromising the successful implantation of the embryo. Nonetheless, with the acquisition of images through the time-lapse system, it is possible to perform digital processing of these images, providing a better analysis of the embryo, in addition to enabling the automatic analysis of a large volume of information. An image processing protocol was developed using well-established techniques to segment the image of blastocysts and extract variables of interest. A total of 33 variables were automatically generated by digital image processing, each one representing a different aspect of the embryo and describing a different characteristic of the blastocyst. These variables can be categorized into texture, gray-level average, gray-level standard deviation, modal value, relations, and light level. The automated and directed steps of the proposed processing protocol exclude spurious results, except when image quality (e.g., focus) prevents correct segmentation. The image processing protocol can segment human blastocyst images and automatically extract 33 variables that describe quantitative aspects of the blastocyst’s regions, with potential utility in embryo selection for assisted reproductive technology (ART). |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-01 2023-03-01T21:19:02Z 2023-03-01T21:19:02Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.3390/app12073531 Applied Sciences (Switzerland), v. 12, n. 7, 2022. 2076-3417 http://hdl.handle.net/11449/241734 10.3390/app12073531 2-s2.0-85128219618 |
url |
http://dx.doi.org/10.3390/app12073531 http://hdl.handle.net/11449/241734 |
identifier_str_mv |
Applied Sciences (Switzerland), v. 12, n. 7, 2022. 2076-3417 10.3390/app12073531 2-s2.0-85128219618 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Applied Sciences (Switzerland) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129191338573824 |