An ensemble method for nuclei detection of overlapping cervical cells

The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. T...

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Vydané v:	Expert systems with applications Ročník 185; s. 115642
Hlavní autori:	Diniz, Débora Nasser, Vitor, Rafael Ferreira, Bianchi, Andrea Gomes Campos, Delabrida, Saul, Carneiro, Cláudia Martins, Ushizima, Daniela Mayumi, de Medeiros, Fátima Nelsizeuma Sombra, Souza, Marcone Jamilson Freitas
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Elsevier Ltd 15.12.2021 Elsevier BV Elsevier
Predmet:	Abnormalities Algorithms Centroids Cervical cancer Cytology Decision trees Detection approach Ensemble methods Feature extraction Image processing algorithm Image segmentation MATHEMATICS AND COMPUTING Nuclei (cytology) Pap smear test Recall Detection approach Ensemble methods Image processing algorithm Cervical cancer Pap smear test
ISSN:	0957-4174, 1873-6793
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Abstract	The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. Therefore there is hope that a support decision system could assist with clinical diagnosis, for example, by identifying sub-cellular abnormalities, such as changes in the nuclei features. This work proposes an ensemble method for cervical nuclei detection aiming to reduce the workload of cytopathologists. First, a preprocessing phase divides the original image into superpixels, which are input to feature extraction and selection algorithms. The proposed ensemble method combines three classifiers: Decision Tree (DT), Nearest Centroid (NC), and k-Nearest Neighbors (k-NN), which are evaluated against the ISBI’14 Overlapping Cervical Cytology Image Segmentation Challenge dataset. Experiments show that the proposed method is the state-of-the-art algorithm of the literature for recall (0.999) and F1 values (0.993). It produced a recall very close to the optimum value and also kept high precision (0.988). [Display omitted] •Ensemble of classifiers to provide a more assertive cervical cell nuclei segmentation.•Automatic inspection of Pap smear test to reduce the human workload in cervical cell analysis.•Superpixel computation and feature extraction to improve cell characterization and classification.
AbstractList	The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. Therefore the expectation is that a support decision system could assist with screening the most relevant cells, for example, by identifying sub-cellular abnormalities, such as changes in the nuclei features. Here, this work proposes a computational method for cervical nuclei detection aimed to reduce the workload of cytopathologists. The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. Therefore there is hope that a support decision system could assist with clinical diagnosis, for example, by identifying sub-cellular abnormalities, such as changes in the nuclei features. This work proposes an ensemble method for cervical nuclei detection aiming to reduce the workload of cytopathologists. First, a preprocessing phase divides the original image into superpixels, which are input to feature extraction and selection algorithms. The proposed ensemble method combines three classifiers: Decision Tree (DT), Nearest Centroid (NC), and k-Nearest Neighbors (k-NN), which are evaluated against the ISBI'14 Overlapping Cervical Cytology Image Segmentation Challenge dataset. Experiments show that the proposed method is the state-of-the-art algorithm of the literature for recall (0.999) and F1 values (0.993). It produced a recall very close to the optimum value and also kept high precision (0.988). The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. Therefore there is hope that a support decision system could assist with clinical diagnosis, for example, by identifying sub-cellular abnormalities, such as changes in the nuclei features. This work proposes an ensemble method for cervical nuclei detection aiming to reduce the workload of cytopathologists. First, a preprocessing phase divides the original image into superpixels, which are input to feature extraction and selection algorithms. The proposed ensemble method combines three classifiers: Decision Tree (DT), Nearest Centroid (NC), and k-Nearest Neighbors (k-NN), which are evaluated against the ISBI’14 Overlapping Cervical Cytology Image Segmentation Challenge dataset. Experiments show that the proposed method is the state-of-the-art algorithm of the literature for recall (0.999) and F1 values (0.993). It produced a recall very close to the optimum value and also kept high precision (0.988). [Display omitted] •Ensemble of classifiers to provide a more assertive cervical cell nuclei segmentation.•Automatic inspection of Pap smear test to reduce the human workload in cervical cell analysis.•Superpixel computation and feature extraction to improve cell characterization and classification.
ArticleNumber	115642
Author	Ushizima, Daniela Mayumi Souza, Marcone Jamilson Freitas Diniz, Débora Nasser Vitor, Rafael Ferreira Carneiro, Cláudia Martins Delabrida, Saul de Medeiros, Fátima Nelsizeuma Sombra Bianchi, Andrea Gomes Campos
Author_xml	– sequence: 1 givenname: Débora Nasser orcidid: 0000-0002-1951-8868 surname: Diniz fullname: Diniz, Débora Nasser email: debora.diniz@aluno.ufop.edu.br organization: Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), ZIP Code: 35400-000, Ouro Preto, MG, Brazil – sequence: 2 givenname: Rafael Ferreira orcidid: 0000-0001-6904-8414 surname: Vitor fullname: Vitor, Rafael Ferreira email: rafael.vitor@aluno.ufop.edu.br organization: Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), ZIP Code: 35400-000, Ouro Preto, MG, Brazil – sequence: 3 givenname: Andrea Gomes Campos orcidid: 0000-0001-7949-1188 surname: Bianchi fullname: Bianchi, Andrea Gomes Campos email: andrea@ufop.edu.br organization: Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), ZIP Code: 35400-000, Ouro Preto, MG, Brazil – sequence: 4 givenname: Saul orcidid: 0000-0002-8961-5313 surname: Delabrida fullname: Delabrida, Saul email: saul.delabrida@ufop.edu.br organization: Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), ZIP Code: 35400-000, Ouro Preto, MG, Brazil – sequence: 5 givenname: Cláudia Martins orcidid: 0000-0002-6002-857X surname: Carneiro fullname: Carneiro, Cláudia Martins email: carneirocm@ufop.edu.br organization: Departamento de Análises Clínicas, Universidade Federal de Ouro Preto (UFOP), ZIP Code: 35400-000, Ouro Preto, MG, Brazil – sequence: 6 givenname: Daniela Mayumi orcidid: 0000-0002-7363-9468 surname: Ushizima fullname: Ushizima, Daniela Mayumi email: dushizima@lbl.gov organization: Computational Research Division, Lawrence Berkeley National Laboratory, ZIP Code: 94720, Berkeley, CA, United States – sequence: 7 givenname: Fátima Nelsizeuma Sombra orcidid: 0000-0003-3075-8771 surname: de Medeiros fullname: de Medeiros, Fátima Nelsizeuma Sombra email: fsombra@ufc.br organization: Departamento de Engenharia de Teleinformática, Universidade Federal do Ceará (UFC), ZIP Code: 60455-970, Fortaleza, CE, Brazil – sequence: 8 givenname: Marcone Jamilson Freitas orcidid: 0000-0002-7141-357X surname: Souza fullname: Souza, Marcone Jamilson Freitas email: marcone@ufop.edu.br organization: Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), ZIP Code: 35400-000, Ouro Preto, MG, Brazil
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Cites_doi	10.1158/1055-9965.EPI-09-1038 10.1159/000478770 10.1016/j.orp.2016.09.002 10.38094/jastt1224 10.14209/sbrt.2017.48 10.1109/JBHI.2016.2519686 10.2307/1932409 10.1109/TMI.2018.2815013 10.1016/j.eswa.2016.08.015 10.1016/j.compmedimag.2019.01.003 10.1109/TIP.2018.2857001 10.1016/j.neucom.2016.09.070 10.1016/j.compbiomed.2019.03.011 10.1142/S1469026810002860 10.1016/j.eswa.2019.112951 10.1109/JBHI.2018.2878945 10.1109/TIP.2015.2389619 10.1016/j.eswa.2011.06.034 10.1016/j.eswa.2018.07.010 10.1023/A:1010933404324 10.1073/pnas.082099299 10.1016/j.patcog.2012.05.006 10.2307/1412159 10.1002/cncr.2820710405 10.1109/JBHI.2018.2885544 10.1080/00031305.1992.10475879
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Keywords	Detection approach Ensemble methods Image processing algorithm Cervical cancer Pap smear test
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Snippet	The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous...
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SubjectTerms	Abnormalities Algorithms Centroids Cervical cancer Cytology Decision trees Detection approach Ensemble methods Feature extraction Image processing algorithm Image segmentation MATHEMATICS AND COMPUTING Nuclei (cytology) Pap smear test Recall
Title	An ensemble method for nuclei detection of overlapping cervical cells
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