Automated Nuclei Segmentation in Dysplastic Histopathological Oral Tissues Using Deep Neural Networks
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/10.1007/978-3-030-33904-3_34 http://hdl.handle.net/11449/198203 |
Resumo: | Dysplasia is a common pre-cancerous abnormality that can be categorized as mild, moderate and severe. With the advance of digital systems applied in microscopes for histological analysis, specialists can obtain data that allows investigation using computational algorithms. These systems are known as computer-aided diagnosis, which provide quantitative analysis in a large number of data and features. This work proposes a method for nuclei segmentation for histopathological images of oral dysplasias based on an artificial neural network model and post-processing stage. This method employed nuclei masks for the training, where objects and bounding boxes were evaluated. In the post-processing step, false positive areas were removed by applying morphological operations, such as dilation and erosion. This approach was applied in a dataset with 296 regions of mice tongue images. The metrics accuracy, sensitivity, specificity, the Dice coefficient and correspondence ratio were employed for evaluation and comparison with other methods present in the literature. The results show that the method was able to segment the images with accuracy average value of 89.52 \pm 0.04 and Dice coefficient of 84.03\pm 0.06. These values are important to indicate that the proposed method can be applied as a tool for nuclei analysis in oral cavity images with relevant precision values for the specialist. |
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Automated Nuclei Segmentation in Dysplastic Histopathological Oral Tissues Using Deep Neural NetworksCADConvolutional neural networkDysplasiaNuclei segmentationDysplasia is a common pre-cancerous abnormality that can be categorized as mild, moderate and severe. With the advance of digital systems applied in microscopes for histological analysis, specialists can obtain data that allows investigation using computational algorithms. These systems are known as computer-aided diagnosis, which provide quantitative analysis in a large number of data and features. This work proposes a method for nuclei segmentation for histopathological images of oral dysplasias based on an artificial neural network model and post-processing stage. This method employed nuclei masks for the training, where objects and bounding boxes were evaluated. In the post-processing step, false positive areas were removed by applying morphological operations, such as dilation and erosion. This approach was applied in a dataset with 296 regions of mice tongue images. The metrics accuracy, sensitivity, specificity, the Dice coefficient and correspondence ratio were employed for evaluation and comparison with other methods present in the literature. The results show that the method was able to segment the images with accuracy average value of 89.52 \pm 0.04 and Dice coefficient of 84.03\pm 0.06. These values are important to indicate that the proposed method can be applied as a tool for nuclei analysis in oral cavity images with relevant precision values for the specialist.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Faculty of Computer Science Federal University of UberlândiaFederal Institute of Triângulo MineiroDepartment of Computer Science and Statistics São Paulo State University (UNESP)Department of Histology and Morphology Institute of Biomedical Science Federal University of UberlândiaCenter of Mathematics Computing and Cognition Federal University of ABCDepartment of Computer Science and Statistics São Paulo State University (UNESP)CNPq: 304848/2018-2CNPq: 313365/2018-0CNPq: 427114/2016-0CNPq: 430965/2018-4FAPEMIG: APQ-00578-18Universidade Federal de Uberlândia (UFU)Federal Institute of Triângulo MineiroUniversidade Estadual Paulista (Unesp)Federal University of ABCSilva, Adriano BarbosaMartins, Alessandro S.Neves, Leandro A. [UNESP]Faria, Paulo R.Tosta, Thaína A. A.do Nascimento, Marcelo Zanchetta2020-12-12T01:06:21Z2020-12-12T01:06:21Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject365-374http://dx.doi.org/10.1007/978-3-030-33904-3_34Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11896 LNCS, p. 365-374.1611-33490302-9743http://hdl.handle.net/11449/19820310.1007/978-3-030-33904-3_342-s2.0-85075660898Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2021-10-23T09:55:31Zoai:repositorio.unesp.br:11449/198203Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T09:55:31Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Automated Nuclei Segmentation in Dysplastic Histopathological Oral Tissues Using Deep Neural Networks |
title |
Automated Nuclei Segmentation in Dysplastic Histopathological Oral Tissues Using Deep Neural Networks |
spellingShingle |
Automated Nuclei Segmentation in Dysplastic Histopathological Oral Tissues Using Deep Neural Networks Silva, Adriano Barbosa CAD Convolutional neural network Dysplasia Nuclei segmentation |
title_short |
Automated Nuclei Segmentation in Dysplastic Histopathological Oral Tissues Using Deep Neural Networks |
title_full |
Automated Nuclei Segmentation in Dysplastic Histopathological Oral Tissues Using Deep Neural Networks |
title_fullStr |
Automated Nuclei Segmentation in Dysplastic Histopathological Oral Tissues Using Deep Neural Networks |
title_full_unstemmed |
Automated Nuclei Segmentation in Dysplastic Histopathological Oral Tissues Using Deep Neural Networks |
title_sort |
Automated Nuclei Segmentation in Dysplastic Histopathological Oral Tissues Using Deep Neural Networks |
author |
Silva, Adriano Barbosa |
author_facet |
Silva, Adriano Barbosa Martins, Alessandro S. Neves, Leandro A. [UNESP] Faria, Paulo R. Tosta, Thaína A. A. do Nascimento, Marcelo Zanchetta |
author_role |
author |
author2 |
Martins, Alessandro S. Neves, Leandro A. [UNESP] Faria, Paulo R. Tosta, Thaína A. A. do Nascimento, Marcelo Zanchetta |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de Uberlândia (UFU) Federal Institute of Triângulo Mineiro Universidade Estadual Paulista (Unesp) Federal University of ABC |
dc.contributor.author.fl_str_mv |
Silva, Adriano Barbosa Martins, Alessandro S. Neves, Leandro A. [UNESP] Faria, Paulo R. Tosta, Thaína A. A. do Nascimento, Marcelo Zanchetta |
dc.subject.por.fl_str_mv |
CAD Convolutional neural network Dysplasia Nuclei segmentation |
topic |
CAD Convolutional neural network Dysplasia Nuclei segmentation |
description |
Dysplasia is a common pre-cancerous abnormality that can be categorized as mild, moderate and severe. With the advance of digital systems applied in microscopes for histological analysis, specialists can obtain data that allows investigation using computational algorithms. These systems are known as computer-aided diagnosis, which provide quantitative analysis in a large number of data and features. This work proposes a method for nuclei segmentation for histopathological images of oral dysplasias based on an artificial neural network model and post-processing stage. This method employed nuclei masks for the training, where objects and bounding boxes were evaluated. In the post-processing step, false positive areas were removed by applying morphological operations, such as dilation and erosion. This approach was applied in a dataset with 296 regions of mice tongue images. The metrics accuracy, sensitivity, specificity, the Dice coefficient and correspondence ratio were employed for evaluation and comparison with other methods present in the literature. The results show that the method was able to segment the images with accuracy average value of 89.52 \pm 0.04 and Dice coefficient of 84.03\pm 0.06. These values are important to indicate that the proposed method can be applied as a tool for nuclei analysis in oral cavity images with relevant precision values for the specialist. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 2020-12-12T01:06:21Z 2020-12-12T01:06:21Z |
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/10.1007/978-3-030-33904-3_34 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11896 LNCS, p. 365-374. 1611-3349 0302-9743 http://hdl.handle.net/11449/198203 10.1007/978-3-030-33904-3_34 2-s2.0-85075660898 |
url |
http://dx.doi.org/10.1007/978-3-030-33904-3_34 http://hdl.handle.net/11449/198203 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11896 LNCS, p. 365-374. 1611-3349 0302-9743 10.1007/978-3-030-33904-3_34 2-s2.0-85075660898 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
365-374 |
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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1799965042972557312 |