Object detection for automatic cancer cell counting in zebrafish xenografts

Detalhes bibliográficos
Autor(a) principal: Albuquerque, Carina
Data de Publicação: 2021
Outros Autores: Vanneschi, Leonardo, Henriques, Roberto, Castelli, Mauro, Póvoa, Vanda, Fior, Rita, Papanikolaou, Nickolas
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/128517
Resumo: Albuquerque, C., Vanneschi, L., Henriques, R., Castelli, M., Póvoa, V., Fior, R., & Papanikolaou, N. (2021). Object detection for automatic cancer cell counting in zebrafish xenografts. PLoS ONE, 16(11), 1-28. [e0260609]. https://doi.org/10.1371/journal.pone.0260609 -----------------------------------------This work was supported by national funds through FCT (Fundaçâo para a Ciência e a Tecnologia), under project PTDC/CCI-INF/29168/2017 (BINDER). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).
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spelling Object detection for automatic cancer cell counting in zebrafish xenograftsGeneralSDG 3 - Good Health and Well-beingAlbuquerque, C., Vanneschi, L., Henriques, R., Castelli, M., Póvoa, V., Fior, R., & Papanikolaou, N. (2021). Object detection for automatic cancer cell counting in zebrafish xenografts. PLoS ONE, 16(11), 1-28. [e0260609]. https://doi.org/10.1371/journal.pone.0260609 -----------------------------------------This work was supported by national funds through FCT (Fundaçâo para a Ciência e a Tecnologia), under project PTDC/CCI-INF/29168/2017 (BINDER). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).Cell counting is a frequent task in medical research studies. However, it is often performed manually; thus, it is time-consuming and prone to human error. Even so, cell counting automation can be challenging to achieve, especially when dealing with crowded scenes and overlapping cells, assuming different shapes and sizes. In this paper, we introduce a deep learning-based cell detection and quantification methodology to automate the cell counting process in the zebrafish xenograft cancer model, an innovative technique for studying tumor biology and for personalizing medicine. First, we implemented a fine-tuned architecture based on the Faster R-CNN using the Inception ResNet V2 feature extractor. Second, we performed several adjustments to optimize the process, paying attention to constraints such as the presence of overlapped cells, the high number of objects to detect, the heterogeneity of the cells’ size and shape, and the small size of the data set. This method resulted in a median error of approximately 1% of the total number of cell units. These results demonstrate the potential of our novel approach for quantifying cells in poorly labeled images. Compared to traditional Faster R-CNN, our method improved the average precision from 71% to 85% on the studied data set.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNAlbuquerque, CarinaVanneschi, LeonardoHenriques, RobertoCastelli, MauroPóvoa, VandaFior, RitaPapanikolaou, Nickolas2021-11-30T23:54:30Z2021-11-292021-11-29T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article28application/pdfhttp://hdl.handle.net/10362/128517eng1932-6203PURE: 35126842https://doi.org/10.1371/journal.pone.0260609info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:08:04Zoai:run.unl.pt:10362/128517Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:19.465227Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Object detection for automatic cancer cell counting in zebrafish xenografts
title Object detection for automatic cancer cell counting in zebrafish xenografts
spellingShingle Object detection for automatic cancer cell counting in zebrafish xenografts
Albuquerque, Carina
General
SDG 3 - Good Health and Well-being
title_short Object detection for automatic cancer cell counting in zebrafish xenografts
title_full Object detection for automatic cancer cell counting in zebrafish xenografts
title_fullStr Object detection for automatic cancer cell counting in zebrafish xenografts
title_full_unstemmed Object detection for automatic cancer cell counting in zebrafish xenografts
title_sort Object detection for automatic cancer cell counting in zebrafish xenografts
author Albuquerque, Carina
author_facet Albuquerque, Carina
Vanneschi, Leonardo
Henriques, Roberto
Castelli, Mauro
Póvoa, Vanda
Fior, Rita
Papanikolaou, Nickolas
author_role author
author2 Vanneschi, Leonardo
Henriques, Roberto
Castelli, Mauro
Póvoa, Vanda
Fior, Rita
Papanikolaou, Nickolas
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Albuquerque, Carina
Vanneschi, Leonardo
Henriques, Roberto
Castelli, Mauro
Póvoa, Vanda
Fior, Rita
Papanikolaou, Nickolas
dc.subject.por.fl_str_mv General
SDG 3 - Good Health and Well-being
topic General
SDG 3 - Good Health and Well-being
description Albuquerque, C., Vanneschi, L., Henriques, R., Castelli, M., Póvoa, V., Fior, R., & Papanikolaou, N. (2021). Object detection for automatic cancer cell counting in zebrafish xenografts. PLoS ONE, 16(11), 1-28. [e0260609]. https://doi.org/10.1371/journal.pone.0260609 -----------------------------------------This work was supported by national funds through FCT (Fundaçâo para a Ciência e a Tecnologia), under project PTDC/CCI-INF/29168/2017 (BINDER). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).
publishDate 2021
dc.date.none.fl_str_mv 2021-11-30T23:54:30Z
2021-11-29
2021-11-29T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/128517
url http://hdl.handle.net/10362/128517
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1932-6203
PURE: 35126842
https://doi.org/10.1371/journal.pone.0260609
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 28
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instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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