Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-030-32965-5_8 http://hdl.handle.net/11449/201365 |
Resumo: | We develop an online graphical and intuitive interface connected to a server aiming to facilitate access to professionals worldwide that face problems with bovine blastocysts classification. The interface Blasto3Q (3Q is referred to the three qualities of the blastocyst grading) contains a description of 24 variables that are extracted from the image of the blastocyst and analyzed by three Artificial Neural Networks (ANNs) that classifies 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 40x of magnification) and to experimental group (stereomicroscope with maximum of magnification plus 4x zoom from the cell phone). The 36 images obtained from control and experimental groups were uploaded on the Blasto3Q. Each image from both sources was evaluated for segmentation and submitted (only if it could be properly or partially segmented) to the quality grade classification by the three ANNs of the Blasto3Q program. In the group control, all the images were properly segmented, whereas 38.9% (07/18) and 61.1% (11/18) of the images from the experimental group, respectively could not be segmented or were partially segmented. The percentage of agreement was calculated when the same blastocyst was evaluated by the same ANN from the two sources (control and experimental groups). On the 54 potential evaluations of the three ANNs (i.e., 18 images been evaluated by the three networks) from the experimental group only 22.2% agreed with evaluations of the control (12/54). Of the remaining 42 disagreed evaluations from experimental group, 21 were unable to be performed and 21 were wrongly processed when compared with control evaluation. |
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Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural NetworkWe develop an online graphical and intuitive interface connected to a server aiming to facilitate access to professionals worldwide that face problems with bovine blastocysts classification. The interface Blasto3Q (3Q is referred to the three qualities of the blastocyst grading) contains a description of 24 variables that are extracted from the image of the blastocyst and analyzed by three Artificial Neural Networks (ANNs) that classifies 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 40x of magnification) and to experimental group (stereomicroscope with maximum of magnification plus 4x zoom from the cell phone). The 36 images obtained from control and experimental groups were uploaded on the Blasto3Q. Each image from both sources was evaluated for segmentation and submitted (only if it could be properly or partially segmented) to the quality grade classification by the three ANNs of the Blasto3Q program. In the group control, all the images were properly segmented, whereas 38.9% (07/18) and 61.1% (11/18) of the images from the experimental group, respectively could not be segmented or were partially segmented. The percentage of agreement was calculated when the same blastocyst was evaluated by the same ANN from the two sources (control and experimental groups). On the 54 potential evaluations of the three ANNs (i.e., 18 images been evaluated by the three networks) from the experimental group only 22.2% agreed with evaluations of the control (12/54). Of the remaining 42 disagreed evaluations from experimental group, 21 were unable to be performed and 21 were wrongly processed when compared with control evaluation.Laboratório de Micromanipulação Embrionária School of Sciences and Languages Universidade Estadual Paulista (Unesp), Av. Dom Antonio 2100Laboratório Multiusuário FitoFarmaTec Institute of Biosciences Unesp, Rubião Jr.Laboratório de Matemática Aplicada School of Sciences and Languages Unesp, Av. Dom Antonio 2100Laboratório de Micromanipulação Embrionária School of Sciences and Languages Universidade Estadual Paulista (Unesp), Av. Dom Antonio 2100Laboratório Multiusuário FitoFarmaTec Institute of Biosciences Unesp, Rubião Jr.Laboratório de Matemática Aplicada School of Sciences and Languages Unesp, Av. Dom Antonio 2100Universidade Estadual Paulista (Unesp)Guilherme, Vitória Bertogna [UNESP]Pronunciate, Micheli [UNESP]dos Santos, Priscila Helena [UNESP]de Souza Ciniciato, Diego [UNESP]Takahashi, Maria Beatriz [UNESP]Rocha, José Celso [UNESP]Gouveia Nogueira, Marcelo Fábio [UNESP]2020-12-12T02:30:41Z2020-12-12T02:30:41Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject139-153http://dx.doi.org/10.1007/978-3-030-32965-5_8Communications in Computer and Information Science, v. 654, p. 139-153.1865-09371865-0929http://hdl.handle.net/11449/20136510.1007/978-3-030-32965-5_82-s2.0-85075808366Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengCommunications in Computer and Information Scienceinfo:eu-repo/semantics/openAccess2024-06-13T17:39:06Zoai:repositorio.unesp.br:11449/201365Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:54:35.994404Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network |
title |
Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network |
spellingShingle |
Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network Guilherme, Vitória Bertogna [UNESP] |
title_short |
Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network |
title_full |
Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network |
title_fullStr |
Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network |
title_full_unstemmed |
Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network |
title_sort |
Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network |
author |
Guilherme, Vitória Bertogna [UNESP] |
author_facet |
Guilherme, Vitória Bertogna [UNESP] Pronunciate, Micheli [UNESP] dos Santos, Priscila Helena [UNESP] de Souza Ciniciato, Diego [UNESP] Takahashi, Maria Beatriz [UNESP] Rocha, José Celso [UNESP] Gouveia Nogueira, Marcelo Fábio [UNESP] |
author_role |
author |
author2 |
Pronunciate, Micheli [UNESP] dos Santos, Priscila Helena [UNESP] de Souza Ciniciato, Diego [UNESP] Takahashi, Maria Beatriz [UNESP] Rocha, José Celso [UNESP] Gouveia Nogueira, Marcelo Fábio [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Guilherme, Vitória Bertogna [UNESP] Pronunciate, Micheli [UNESP] dos Santos, Priscila Helena [UNESP] de Souza Ciniciato, Diego [UNESP] Takahashi, Maria Beatriz [UNESP] Rocha, José Celso [UNESP] Gouveia Nogueira, Marcelo Fábio [UNESP] |
description |
We develop an online graphical and intuitive interface connected to a server aiming to facilitate access to professionals worldwide that face problems with bovine blastocysts classification. The interface Blasto3Q (3Q is referred to the three qualities of the blastocyst grading) contains a description of 24 variables that are extracted from the image of the blastocyst and analyzed by three Artificial Neural Networks (ANNs) that classifies 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 40x of magnification) and to experimental group (stereomicroscope with maximum of magnification plus 4x zoom from the cell phone). The 36 images obtained from control and experimental groups were uploaded on the Blasto3Q. Each image from both sources was evaluated for segmentation and submitted (only if it could be properly or partially segmented) to the quality grade classification by the three ANNs of the Blasto3Q program. In the group control, all the images were properly segmented, whereas 38.9% (07/18) and 61.1% (11/18) of the images from the experimental group, respectively could not be segmented or were partially segmented. The percentage of agreement was calculated when the same blastocyst was evaluated by the same ANN from the two sources (control and experimental groups). On the 54 potential evaluations of the three ANNs (i.e., 18 images been evaluated by the three networks) from the experimental group only 22.2% agreed with evaluations of the control (12/54). Of the remaining 42 disagreed evaluations from experimental group, 21 were unable to be performed and 21 were wrongly processed when compared with control evaluation. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 2020-12-12T02:30:41Z 2020-12-12T02:30:41Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-030-32965-5_8 Communications in Computer and Information Science, v. 654, p. 139-153. 1865-0937 1865-0929 http://hdl.handle.net/11449/201365 10.1007/978-3-030-32965-5_8 2-s2.0-85075808366 |
url |
http://dx.doi.org/10.1007/978-3-030-32965-5_8 http://hdl.handle.net/11449/201365 |
identifier_str_mv |
Communications in Computer and Information Science, v. 654, p. 139-153. 1865-0937 1865-0929 10.1007/978-3-030-32965-5_8 2-s2.0-85075808366 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Communications in Computer and Information Science |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
139-153 |
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 |
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1808128719455256576 |