Automatic detection of endothelial cells in 3D angiogenic sprouts from experimental phase contrast images
Author(s)
Wang, MengMeng; Ong, Lee-Ling Sharon; Dauwels, Justin; Asada, Haruhiko
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Cell 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.
Date issued
2015-03Department
Massachusetts Institute of Technology. Department of Mechanical EngineeringJournal
Proceedings of SPIE--the Society of Photo-Optical Instrumentation Engineers
Publisher
SPIE
Citation
Wang, MengMeng et al. “Automatic Detection of Endothelial Cells in 3D Angiogenic Sprouts from Experimental Phase Contrast Images.” Ed. Sébastien Ourselin and Martin A. Styner. N.p., 2015. 94132I. © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Version: Final published version
ISSN
9781628415032