Segmentation of Oral Epithelial Dysplasias Employing Mask R-CNN and Color Normalization
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/BIBM49941.2020.9313101 http://hdl.handle.net/11449/207224 |
Resumo: | Oral epithelial dysplasia is a common type of pre-cancerous lesion that can be categorized as mild, moderate and severe. The manual diagnosis of this type of lesion is a time consuming and complex task. The use of digital systems applied to microscopic image analysis can aid the decision making of specialists. In recent years, deep learning-based methods are getting more attention due to its improved results in nuclei segmentation tasks. In this paper, we propose a methodology for nuclei segmentation on images of dysplastic tissues using neural networks. Several optimization algorithms and color normalization methods were evaluated. The methodology was performed on a dataset of mice tongue images. The experimental evaluations showed that the Nadam optimizer in combination with images without the use of color normalization obtained the best results. The method was able to segment the images with an average accuracy of 0.887, the sensitivity of 0.762 and specificity of 0.942. The algorithm was compared to other segmentation methods and showed relevant results. These values indicate that the proposed method can be used as a tool to aid specialists in the nuclei analysis of histological images of the buccal cavity. |
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Segmentation of Oral Epithelial Dysplasias Employing Mask R-CNN and Color Normalizationcolor normalizationconvolutional neural networkDysplasianuclei segmentation.Oral epithelial dysplasia is a common type of pre-cancerous lesion that can be categorized as mild, moderate and severe. The manual diagnosis of this type of lesion is a time consuming and complex task. The use of digital systems applied to microscopic image analysis can aid the decision making of specialists. In recent years, deep learning-based methods are getting more attention due to its improved results in nuclei segmentation tasks. In this paper, we propose a methodology for nuclei segmentation on images of dysplastic tissues using neural networks. Several optimization algorithms and color normalization methods were evaluated. The methodology was performed on a dataset of mice tongue images. The experimental evaluations showed that the Nadam optimizer in combination with images without the use of color normalization obtained the best results. The method was able to segment the images with an average accuracy of 0.887, the sensitivity of 0.762 and specificity of 0.942. The algorithm was compared to other segmentation methods and showed relevant results. These values indicate that the proposed method can be used as a tool to aid specialists in the nuclei analysis of histological images of the buccal cavity.Facom UfuIct UnifespIftmDcce UnespDcce UnespFacom UfuUniversidade Federal de São Paulo (UNIFESP)IftmUniversidade Estadual Paulista (Unesp)Silva, Adriano B.Santos, Dali F. D. DosTosta, Thaina A. A.Martins, Alessandro S.Neves, Leandro A. [UNESP]Travenclo, Bruno A. N.Faria, Paulo R. DeNascimento, Marcelo Z. Do2021-06-25T10:50:58Z2021-06-25T10:50:58Z2020-12-16info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject2818-2824http://dx.doi.org/10.1109/BIBM49941.2020.9313101Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, p. 2818-2824.http://hdl.handle.net/11449/20722410.1109/BIBM49941.2020.93131012-s2.0-85100346447Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020info:eu-repo/semantics/openAccess2021-10-23T16:36:59Zoai:repositorio.unesp.br:11449/207224Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T16:36:59Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Segmentation of Oral Epithelial Dysplasias Employing Mask R-CNN and Color Normalization |
title |
Segmentation of Oral Epithelial Dysplasias Employing Mask R-CNN and Color Normalization |
spellingShingle |
Segmentation of Oral Epithelial Dysplasias Employing Mask R-CNN and Color Normalization Silva, Adriano B. color normalization convolutional neural network Dysplasia nuclei segmentation. |
title_short |
Segmentation of Oral Epithelial Dysplasias Employing Mask R-CNN and Color Normalization |
title_full |
Segmentation of Oral Epithelial Dysplasias Employing Mask R-CNN and Color Normalization |
title_fullStr |
Segmentation of Oral Epithelial Dysplasias Employing Mask R-CNN and Color Normalization |
title_full_unstemmed |
Segmentation of Oral Epithelial Dysplasias Employing Mask R-CNN and Color Normalization |
title_sort |
Segmentation of Oral Epithelial Dysplasias Employing Mask R-CNN and Color Normalization |
author |
Silva, Adriano B. |
author_facet |
Silva, Adriano B. Santos, Dali F. D. Dos Tosta, Thaina A. A. Martins, Alessandro S. Neves, Leandro A. [UNESP] Travenclo, Bruno A. N. Faria, Paulo R. De Nascimento, Marcelo Z. Do |
author_role |
author |
author2 |
Santos, Dali F. D. Dos Tosta, Thaina A. A. Martins, Alessandro S. Neves, Leandro A. [UNESP] Travenclo, Bruno A. N. Faria, Paulo R. De Nascimento, Marcelo Z. Do |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Facom Ufu Universidade Federal de São Paulo (UNIFESP) Iftm Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Silva, Adriano B. Santos, Dali F. D. Dos Tosta, Thaina A. A. Martins, Alessandro S. Neves, Leandro A. [UNESP] Travenclo, Bruno A. N. Faria, Paulo R. De Nascimento, Marcelo Z. Do |
dc.subject.por.fl_str_mv |
color normalization convolutional neural network Dysplasia nuclei segmentation. |
topic |
color normalization convolutional neural network Dysplasia nuclei segmentation. |
description |
Oral epithelial dysplasia is a common type of pre-cancerous lesion that can be categorized as mild, moderate and severe. The manual diagnosis of this type of lesion is a time consuming and complex task. The use of digital systems applied to microscopic image analysis can aid the decision making of specialists. In recent years, deep learning-based methods are getting more attention due to its improved results in nuclei segmentation tasks. In this paper, we propose a methodology for nuclei segmentation on images of dysplastic tissues using neural networks. Several optimization algorithms and color normalization methods were evaluated. The methodology was performed on a dataset of mice tongue images. The experimental evaluations showed that the Nadam optimizer in combination with images without the use of color normalization obtained the best results. The method was able to segment the images with an average accuracy of 0.887, the sensitivity of 0.762 and specificity of 0.942. The algorithm was compared to other segmentation methods and showed relevant results. These values indicate that the proposed method can be used as a tool to aid specialists in the nuclei analysis of histological images of the buccal cavity. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-16 2021-06-25T10:50:58Z 2021-06-25T10:50:58Z |
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/10.1109/BIBM49941.2020.9313101 Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, p. 2818-2824. http://hdl.handle.net/11449/207224 10.1109/BIBM49941.2020.9313101 2-s2.0-85100346447 |
url |
http://dx.doi.org/10.1109/BIBM49941.2020.9313101 http://hdl.handle.net/11449/207224 |
identifier_str_mv |
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, p. 2818-2824. 10.1109/BIBM49941.2020.9313101 2-s2.0-85100346447 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
2818-2824 |
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_ |
1799964875101831168 |