Automated CTC Classification, Enumeration and Pheno Typing: Where Math meets Biology

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

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Abstract

Circulating tumor cells (CTCs) found in the blood of cancer patients are a promising biomarker in precision medicine. Yet, CTCs are very rare and it is very challenging to manually detect, characterize and count them in images of fluorescently labeled cells. Therefore, this thesis aims to develop image analysis and machine learning models to automatically and accurately detect and classify CTCs in fluorescent images and prove the added benefit of automated methods for cancer patients. CTCs and their fluorescent signals strongly vary in size, shape and signal intensity. We addressed the presence of multiple size and intensity scales by the development of a multiscale segmentation model. We combined nonlinear segmentation with scale spaces and spectral decompositions. This results in an accurate segmentation of the cells combined with a cell clustering based on their size and intensity. We further adapted the model to a purely size-based clustering of the cells which excels in segmenting also very dim fluorescent signals. Based on the segmentation of all cells, we extracted quantifiable features of the immunofluorescent expression of each cell. These features were used for an accurate and reproducible assessment of expression levels of CTC treatment targets. Moreover, they allowed the identification of new cell populations which were also overexpressed by cancer patients compared to healthy subjects. We further analyzed the consensus of multiple reviewers in manually scoring cells as a “CTC” or “no CTC”. Their significant disagreement motivated the development of an automated learning algorithm to reproducibly classify CTCs and other cell populations of interest. Therefore, we used a convolutional neural network with an architecture based on autoencoders and combined it with an advanced visualization of the low-dimensional latent space. The chosen network allows to not only classify cells but to reveal new cell populations and subclasses of known populations. The work presented in this thesis resulted in the development of an open-source software for CTC analysis called ACCEPT (Automated CTC Classification, Enumeration and PhenoTyping). The toolbox facilitates and automates the process of detecting and classifying CTCs and other cell populations in fluorescent images.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Terstappen, Leon, Supervisor
  • van Gils, Stephanus A., Supervisor
  • Brune, Christoph , Co-Supervisor
Award date14 Feb 2019
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-4713-0
DOIs
Publication statusPublished - 14 Feb 2019

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Tumors
Cells
Biomarkers
Image analysis
Learning algorithms
Medicine
Learning systems
Blood
Visualization
Neural networks
Decomposition

Cite this

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title = "Automated CTC Classification, Enumeration and Pheno Typing: Where Math meets Biology",
abstract = "Circulating tumor cells (CTCs) found in the blood of cancer patients are a promising biomarker in precision medicine. Yet, CTCs are very rare and it is very challenging to manually detect, characterize and count them in images of fluorescently labeled cells. Therefore, this thesis aims to develop image analysis and machine learning models to automatically and accurately detect and classify CTCs in fluorescent images and prove the added benefit of automated methods for cancer patients. CTCs and their fluorescent signals strongly vary in size, shape and signal intensity. We addressed the presence of multiple size and intensity scales by the development of a multiscale segmentation model. We combined nonlinear segmentation with scale spaces and spectral decompositions. This results in an accurate segmentation of the cells combined with a cell clustering based on their size and intensity. We further adapted the model to a purely size-based clustering of the cells which excels in segmenting also very dim fluorescent signals. Based on the segmentation of all cells, we extracted quantifiable features of the immunofluorescent expression of each cell. These features were used for an accurate and reproducible assessment of expression levels of CTC treatment targets. Moreover, they allowed the identification of new cell populations which were also overexpressed by cancer patients compared to healthy subjects. We further analyzed the consensus of multiple reviewers in manually scoring cells as a “CTC” or “no CTC”. Their significant disagreement motivated the development of an automated learning algorithm to reproducibly classify CTCs and other cell populations of interest. Therefore, we used a convolutional neural network with an architecture based on autoencoders and combined it with an advanced visualization of the low-dimensional latent space. The chosen network allows to not only classify cells but to reveal new cell populations and subclasses of known populations. The work presented in this thesis resulted in the development of an open-source software for CTC analysis called ACCEPT (Automated CTC Classification, Enumeration and PhenoTyping). The toolbox facilitates and automates the process of detecting and classifying CTCs and other cell populations in fluorescent images.",
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Automated CTC Classification, Enumeration and Pheno Typing : Where Math meets Biology. / Zeune, Leonie Laura.

Enschede : University of Twente, 2019. 207 p.

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

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T2 - Where Math meets Biology

AU - Zeune, Leonie Laura

PY - 2019/2/14

Y1 - 2019/2/14

N2 - Circulating tumor cells (CTCs) found in the blood of cancer patients are a promising biomarker in precision medicine. Yet, CTCs are very rare and it is very challenging to manually detect, characterize and count them in images of fluorescently labeled cells. Therefore, this thesis aims to develop image analysis and machine learning models to automatically and accurately detect and classify CTCs in fluorescent images and prove the added benefit of automated methods for cancer patients. CTCs and their fluorescent signals strongly vary in size, shape and signal intensity. We addressed the presence of multiple size and intensity scales by the development of a multiscale segmentation model. We combined nonlinear segmentation with scale spaces and spectral decompositions. This results in an accurate segmentation of the cells combined with a cell clustering based on their size and intensity. We further adapted the model to a purely size-based clustering of the cells which excels in segmenting also very dim fluorescent signals. Based on the segmentation of all cells, we extracted quantifiable features of the immunofluorescent expression of each cell. These features were used for an accurate and reproducible assessment of expression levels of CTC treatment targets. Moreover, they allowed the identification of new cell populations which were also overexpressed by cancer patients compared to healthy subjects. We further analyzed the consensus of multiple reviewers in manually scoring cells as a “CTC” or “no CTC”. Their significant disagreement motivated the development of an automated learning algorithm to reproducibly classify CTCs and other cell populations of interest. Therefore, we used a convolutional neural network with an architecture based on autoencoders and combined it with an advanced visualization of the low-dimensional latent space. The chosen network allows to not only classify cells but to reveal new cell populations and subclasses of known populations. The work presented in this thesis resulted in the development of an open-source software for CTC analysis called ACCEPT (Automated CTC Classification, Enumeration and PhenoTyping). The toolbox facilitates and automates the process of detecting and classifying CTCs and other cell populations in fluorescent images.

AB - Circulating tumor cells (CTCs) found in the blood of cancer patients are a promising biomarker in precision medicine. Yet, CTCs are very rare and it is very challenging to manually detect, characterize and count them in images of fluorescently labeled cells. Therefore, this thesis aims to develop image analysis and machine learning models to automatically and accurately detect and classify CTCs in fluorescent images and prove the added benefit of automated methods for cancer patients. CTCs and their fluorescent signals strongly vary in size, shape and signal intensity. We addressed the presence of multiple size and intensity scales by the development of a multiscale segmentation model. We combined nonlinear segmentation with scale spaces and spectral decompositions. This results in an accurate segmentation of the cells combined with a cell clustering based on their size and intensity. We further adapted the model to a purely size-based clustering of the cells which excels in segmenting also very dim fluorescent signals. Based on the segmentation of all cells, we extracted quantifiable features of the immunofluorescent expression of each cell. These features were used for an accurate and reproducible assessment of expression levels of CTC treatment targets. Moreover, they allowed the identification of new cell populations which were also overexpressed by cancer patients compared to healthy subjects. We further analyzed the consensus of multiple reviewers in manually scoring cells as a “CTC” or “no CTC”. Their significant disagreement motivated the development of an automated learning algorithm to reproducibly classify CTCs and other cell populations of interest. Therefore, we used a convolutional neural network with an architecture based on autoencoders and combined it with an advanced visualization of the low-dimensional latent space. The chosen network allows to not only classify cells but to reveal new cell populations and subclasses of known populations. The work presented in this thesis resulted in the development of an open-source software for CTC analysis called ACCEPT (Automated CTC Classification, Enumeration and PhenoTyping). The toolbox facilitates and automates the process of detecting and classifying CTCs and other cell populations in fluorescent images.

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DO - 10.3990/1.9789036547130

M3 - PhD Thesis - Research UT, graduation UT

SN - 978-90-365-4713-0

PB - University of Twente

CY - Enschede

ER -