Artificial intelligence-based grading quality of bovine blastocyst digital images: Direct capture with juxtaposed lenses of smartphone camera and stereomicroscope ocular lens
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/s18124440 http://hdl.handle.net/11449/189970 |
Resumo: | In this study, we developed an online graphical and intuitive interface connected to a server aiming to facilitate professional access worldwide to those facing problems with bovine blastocysts classification. The interface Blasto3Q, where 3Q refers to the three qualities of the blastocyst grading, contains a description of 24 variables that were extracted from the image of the blastocyst and analyzed by three Artificial Neural Networks (ANNs) that classify the same loaded image. The same embryo (i.e., the biological specimen) was submitted to digital image capture by the control group (inverted microscope with 40× magnification) and the experimental group (stereomicroscope with maximum of magnification plus 4× zoom from the cell phone camera). The images obtained from the control and experimental groups were uploaded on Blasto3Q. Each image from both sources was evaluated for segmentation and submitted (only if it could be properly or partially segmented) for automatic quality grade classification by the three ANNs of the Blasto3Q program. Adjustments on the software program through the use of scaling algorithm software were performed to ensure the proper search and segmentation of the embryo in the raw images when they were captured by the smartphone, since this source produced small embryo images compared with those from the inverted microscope. With this new program, 77.8% of the images from smartphones were successfully segmented and from those, 85.7% were evaluated by the Blasto3Q in agreement with the control group. |
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Artificial intelligence-based grading quality of bovine blastocyst digital images: Direct capture with juxtaposed lenses of smartphone camera and stereomicroscope ocular lensArtificial intelligenceArtificial neural networksBovine blastocystDigital image captureEmbryo gradingImage processingSmartphone cameraSoftwareIn this study, we developed an online graphical and intuitive interface connected to a server aiming to facilitate professional access worldwide to those facing problems with bovine blastocysts classification. The interface Blasto3Q, where 3Q refers to the three qualities of the blastocyst grading, contains a description of 24 variables that were extracted from the image of the blastocyst and analyzed by three Artificial Neural Networks (ANNs) that classify the same loaded image. The same embryo (i.e., the biological specimen) was submitted to digital image capture by the control group (inverted microscope with 40× magnification) and the experimental group (stereomicroscope with maximum of magnification plus 4× zoom from the cell phone camera). The images obtained from the control and experimental groups were uploaded on Blasto3Q. Each image from both sources was evaluated for segmentation and submitted (only if it could be properly or partially segmented) for automatic quality grade classification by the three ANNs of the Blasto3Q program. Adjustments on the software program through the use of scaling algorithm software were performed to ensure the proper search and segmentation of the embryo in the raw images when they were captured by the smartphone, since this source produced small embryo images compared with those from the inverted microscope. With this new program, 77.8% of the images from smartphones were successfully segmented and from those, 85.7% were evaluated by the Blasto3Q in agreement with the control group.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Universidade Estadual PaulistaLaboratory of Embryonic Micromanipulation Department of Biological Sciences School of Sciences and Languages São Paulo State University (UNESP)Multiuser Facility (FitoFarmaTec) Department of Pharmacology Biosciences Institute São Paulo State University (UNESP)Laboratory of Applied Mathematics Department of Biological Sciences School of Sciences and Languages São Paulo State University (UNESP)Laboratory of Embryonic Micromanipulation Department of Biological Sciences School of Sciences and Languages São Paulo State University (UNESP)Multiuser Facility (FitoFarmaTec) Department of Pharmacology Biosciences Institute São Paulo State University (UNESP)Laboratory of Applied Mathematics Department of Biological Sciences School of Sciences and Languages São Paulo State University (UNESP)FAPESP: 2006/06491-2FAPESP: 2011/06179-7FAPESP: 2012/20110-2FAPESP: 2012/50533-2FAPESP: 2013-05083-1FAPESP: 2016/19004-4FAPESP: 2017/19323-5Universidade Estadual Paulista (Unesp)Nogueira, Marcelo Fábio Gouveia [UNESP]Guilherme, Vitória Bertogna [UNESP]Pronunciate, Micheli [UNESP]Dos Santos, Priscila Helena [UNESP]da Silva, Diogo Lima Bezerra [UNESP]Rocha, José Celso [UNESP]2019-10-06T16:58:08Z2019-10-06T16:58:08Z2018-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/s18124440Sensors (Switzerland), v. 18, n. 12, 2018.1424-8220http://hdl.handle.net/11449/18997010.3390/s181244402-s2.0-850586217843734933152414412Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSensors (Switzerland)info:eu-repo/semantics/openAccess2024-06-13T17:38:00Zoai:repositorio.unesp.br:11449/189970Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:10:15.926024Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Artificial intelligence-based grading quality of bovine blastocyst digital images: Direct capture with juxtaposed lenses of smartphone camera and stereomicroscope ocular lens |
title |
Artificial intelligence-based grading quality of bovine blastocyst digital images: Direct capture with juxtaposed lenses of smartphone camera and stereomicroscope ocular lens |
spellingShingle |
Artificial intelligence-based grading quality of bovine blastocyst digital images: Direct capture with juxtaposed lenses of smartphone camera and stereomicroscope ocular lens Nogueira, Marcelo Fábio Gouveia [UNESP] Artificial intelligence Artificial neural networks Bovine blastocyst Digital image capture Embryo grading Image processing Smartphone camera Software |
title_short |
Artificial intelligence-based grading quality of bovine blastocyst digital images: Direct capture with juxtaposed lenses of smartphone camera and stereomicroscope ocular lens |
title_full |
Artificial intelligence-based grading quality of bovine blastocyst digital images: Direct capture with juxtaposed lenses of smartphone camera and stereomicroscope ocular lens |
title_fullStr |
Artificial intelligence-based grading quality of bovine blastocyst digital images: Direct capture with juxtaposed lenses of smartphone camera and stereomicroscope ocular lens |
title_full_unstemmed |
Artificial intelligence-based grading quality of bovine blastocyst digital images: Direct capture with juxtaposed lenses of smartphone camera and stereomicroscope ocular lens |
title_sort |
Artificial intelligence-based grading quality of bovine blastocyst digital images: Direct capture with juxtaposed lenses of smartphone camera and stereomicroscope ocular lens |
author |
Nogueira, Marcelo Fábio Gouveia [UNESP] |
author_facet |
Nogueira, Marcelo Fábio Gouveia [UNESP] Guilherme, Vitória Bertogna [UNESP] Pronunciate, Micheli [UNESP] Dos Santos, Priscila Helena [UNESP] da Silva, Diogo Lima Bezerra [UNESP] Rocha, José Celso [UNESP] |
author_role |
author |
author2 |
Guilherme, Vitória Bertogna [UNESP] Pronunciate, Micheli [UNESP] Dos Santos, Priscila Helena [UNESP] da Silva, Diogo Lima Bezerra [UNESP] Rocha, José Celso [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Nogueira, Marcelo Fábio Gouveia [UNESP] Guilherme, Vitória Bertogna [UNESP] Pronunciate, Micheli [UNESP] Dos Santos, Priscila Helena [UNESP] da Silva, Diogo Lima Bezerra [UNESP] Rocha, José Celso [UNESP] |
dc.subject.por.fl_str_mv |
Artificial intelligence Artificial neural networks Bovine blastocyst Digital image capture Embryo grading Image processing Smartphone camera Software |
topic |
Artificial intelligence Artificial neural networks Bovine blastocyst Digital image capture Embryo grading Image processing Smartphone camera Software |
description |
In this study, we developed an online graphical and intuitive interface connected to a server aiming to facilitate professional access worldwide to those facing problems with bovine blastocysts classification. The interface Blasto3Q, where 3Q refers to the three qualities of the blastocyst grading, contains a description of 24 variables that were extracted from the image of the blastocyst and analyzed by three Artificial Neural Networks (ANNs) that classify the same loaded image. The same embryo (i.e., the biological specimen) was submitted to digital image capture by the control group (inverted microscope with 40× magnification) and the experimental group (stereomicroscope with maximum of magnification plus 4× zoom from the cell phone camera). The images obtained from the control and experimental groups were uploaded on Blasto3Q. Each image from both sources was evaluated for segmentation and submitted (only if it could be properly or partially segmented) for automatic quality grade classification by the three ANNs of the Blasto3Q program. Adjustments on the software program through the use of scaling algorithm software were performed to ensure the proper search and segmentation of the embryo in the raw images when they were captured by the smartphone, since this source produced small embryo images compared with those from the inverted microscope. With this new program, 77.8% of the images from smartphones were successfully segmented and from those, 85.7% were evaluated by the Blasto3Q in agreement with the control group. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-01 2019-10-06T16:58:08Z 2019-10-06T16:58:08Z |
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/s18124440 Sensors (Switzerland), v. 18, n. 12, 2018. 1424-8220 http://hdl.handle.net/11449/189970 10.3390/s18124440 2-s2.0-85058621784 3734933152414412 |
url |
http://dx.doi.org/10.3390/s18124440 http://hdl.handle.net/11449/189970 |
identifier_str_mv |
Sensors (Switzerland), v. 18, n. 12, 2018. 1424-8220 10.3390/s18124440 2-s2.0-85058621784 3734933152414412 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sensors (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_ |
1808128328013447168 |