Detection and segmentation of macrophages in Quantitative Phase Images by Deep Learning using a Mask Region-based Convolutional Neural
Autor(a) principal: | |
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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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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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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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1799138045854744577 |