Detection and segmentation of macrophages in Quantitative Phase Images by Deep Learning using a Mask Region-based Convolutional Neural

Detalhes bibliográficos
Autor(a) principal: Kutscher, Tobias
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/117608
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling Detection and segmentation of macrophages in Quantitative Phase Images by Deep Learning using a Mask Region-based Convolutional NeuralDeep LearningMask R-CNNDigital Holographic MicroscopyQuantitative Phase ImagingBiomedical ImagingCell Detection and SegmentationDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceQuantitative Phase Imaging (QPI) has been demonstrated to be a versatile tool for minimally invasive label-free imaging of biological specimens and time-resolved cellular analysis. RAW 264.7 mouse macrophages were imaged by Digital Holographic Microscopy (DHM), an interferometry-based variant of QPI, in toxicological studies and cellular growth experiments. Robust detection and segmentation of cells in QPI images by Deep Learning facilitates automated data evaluation of images in high throughput microscopy. Detection, segmentation and the subsequent analysis of single cellular specimens in QPI images yields essential toxicity related physical parameters like the dry mass on the single-cell level. Deep Learning models, such as the Mask Region-based Convolutional Neural Network (Mask R-CNN), were proven to achieve robust results for object detection in fluorescence microscopy images. Thus, a Mask R-CNN was applied with the aim to obtain deeper cellular knowledge from DHM QPI images. This work shows that the combination of label-free DHM and a state-of-the-art Deep Learning model achieves reliable machine-generated data on the single-cell level and prospects to enhance the information as well as the quality of physical data that can be extracted from QPI images of biomedical experiments and label-free high throughput microscopy.Castelli, MauroEder, KaiRUNKutscher, Tobias2021-05-13T16:26:35Z2021-04-222021-04-22T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/117608TID:202722872enginfo: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:RCAAP2024-03-11T05:00:39Zoai:run.unl.pt:10362/117608Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:43:39.637275Repositório 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 Detection and segmentation of macrophages in Quantitative Phase Images by Deep Learning using a Mask Region-based Convolutional Neural
title Detection and segmentation of macrophages in Quantitative Phase Images by Deep Learning using a Mask Region-based Convolutional Neural
spellingShingle Detection and segmentation of macrophages in Quantitative Phase Images by Deep Learning using a Mask Region-based Convolutional Neural
Kutscher, Tobias
Deep Learning
Mask R-CNN
Digital Holographic Microscopy
Quantitative Phase Imaging
Biomedical Imaging
Cell Detection and Segmentation
title_short Detection and segmentation of macrophages in Quantitative Phase Images by Deep Learning using a Mask Region-based Convolutional Neural
title_full Detection and segmentation of macrophages in Quantitative Phase Images by Deep Learning using a Mask Region-based Convolutional Neural
title_fullStr Detection and segmentation of macrophages in Quantitative Phase Images by Deep Learning using a Mask Region-based Convolutional Neural
title_full_unstemmed Detection and segmentation of macrophages in Quantitative Phase Images by Deep Learning using a Mask Region-based Convolutional Neural
title_sort Detection and segmentation of macrophages in Quantitative Phase Images by Deep Learning using a Mask Region-based Convolutional Neural
author Kutscher, Tobias
author_facet Kutscher, Tobias
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
Eder, Kai
RUN
dc.contributor.author.fl_str_mv Kutscher, Tobias
dc.subject.por.fl_str_mv Deep Learning
Mask R-CNN
Digital Holographic Microscopy
Quantitative Phase Imaging
Biomedical Imaging
Cell Detection and Segmentation
topic Deep Learning
Mask R-CNN
Digital Holographic Microscopy
Quantitative Phase Imaging
Biomedical Imaging
Cell Detection and Segmentation
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2021
dc.date.none.fl_str_mv 2021-05-13T16:26:35Z
2021-04-22
2021-04-22T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/117608
TID:202722872
url http://hdl.handle.net/10362/117608
identifier_str_mv TID:202722872
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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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