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Proceedings Paper

Automatic detection of endothelial cells in 3D angiogenic sprouts from experimental phase contrast images
Author(s): MengMeng Wang; Lee-Ling Sharon Ong; Justin Dauwels; H. Harry Asada
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Paper Abstract

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.

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94132I (20 March 2015); doi: 10.1117/12.2081819
Show Author Affiliations
MengMeng Wang, Singapore-MIT Alliance for Research and Technology (Singapore)
Nanyang Technological Univ. (Singapore)
Lee-Ling Sharon Ong, Singapore-MIT Alliance for Research and Technology (Singapore)
Justin Dauwels, Singapore-MIT Alliance for Research and Technology (Singapore)
Nanyang Technological Univ. (Singapore)
H. Harry Asada, Singapore-MIT Alliance for Research and Technology (Singapore)
Massachusetts Institute of Technology (United States)

Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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