Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83651
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dc.contributor.authorWang, MengMengen
dc.contributor.authorOng, Lee-Ling Sharonen
dc.contributor.authorDauwels, Justinen
dc.contributor.authorAsada, H. Harryen
dc.contributor.editorOurselin, Sébastienen
dc.contributor.editorStyner, Martin A.en
dc.date.accessioned2015-12-18T04:22:42Zen
dc.date.accessioned2019-12-06T15:27:32Z-
dc.date.available2015-12-18T04:22:42Zen
dc.date.available2019-12-06T15:27:32Z-
dc.date.issued2015en
dc.identifier.citationWang, M., Ong, L.-L. S., Dauwels, J., & Asada, H. H. (2015). Automatic detection of endothelial cells in 3D angiogenic sprouts from experimental phase contrast images. Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 9413, 94132I-.en
dc.identifier.issn1605-7422en
dc.identifier.urihttps://hdl.handle.net/10356/83651-
dc.description.abstractCell migration studies in 3D environments become more popular, as cell behaviors in 3D are more similar to the behaviors of cells in a living organism (in vivo). We focus on the 3D angiogenic sprouting in microfluidic devices, where Endothelial Cells (ECs) burrow into the gel matrix and form solid lumen vessels. Phase contrast microscopy is used for long-term observation of the unlabeled ECs in the 3D microfluidic devices. Two template matching based approaches are proposed to automatically detect the unlabeled ECs in the angiogenic sprouts from the acquired experimental phase contrast images. Cell and non-cell templates are obtained from these phase contrast images as the training data. The first approach applies Partial Least Square Regression (PLSR) to find the discriminative features and their corresponding weight to distinguish cells and non-cells, whereas the second approach relies on Principal Component Analysis (PCA) to reduce the template feature dimension and Support Vector Machine (SVM) to find their corresponding weight. Through a sliding window manner, the cells in the test images are detected. We then validate the detection accuracy by comparing the results with the same images acquired with a confocal microscope after cells are fixed and their nuclei are stained. More accurate numerical results are obtained for approach I (PLSR) compared to approach II (PCA & SVM) for cell detection. Automatic cell detection will aid in the understanding of cell migration in 3D environment and in turn result in a better understanding of angiogenesis.en
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en
dc.format.extent7 p.en
dc.language.isoenen
dc.relation.ispartofseriesProc. SPIE 9413, Medical Imaging 2015: Image Processingen
dc.rights© 2015 Society of Photo-optical Instrumentation Engineers. This paper was published in Proc. SPIE 9413, Medical Imaging 2015: Image Processing and is made available as an electronic reprint (preprint) with permission of Society of Photo-optical Instrumentation Engineers. The published version is available at: [http://dx.doi.org/10.1117/12.2081819]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en
dc.subjectEndothelial cell detection; 3D angiogenic sprouts; partial least square regression; phase contrast imagesen
dc.titleAutomatic Detection of Endothelial Cells in 3D Angiogenic Sprouts from Experimental Phase Contrast Imagesen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.conferenceSPIE Medical Imagingen
dc.identifier.doi10.1117/12.2081819en
dc.description.versionPublished versionen
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