Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87775
Title: Multicell migration tracking within angiogenic networks by deep learning-based segmentation and augmented Bayesian filtering
Authors: Ong, Sharon Lee-Ling
Dauwels, Justin
Asada, H. Harry
Wang, Mengmeng
Keywords: Backward Kalman Filters
Coarse Time-lapse Phase-contrast Images
Issue Date: 2018
Source: Wang, M., Ong, S. L.-L., Dauwels, J., & Asada, H. H. (2018). Multicell migration tracking within angiogenic networks by deep learning-based segmentation and augmented Bayesian filtering. Journal of Medical Imaging, 5(02), 024005-.
Series/Report no.: Journal of Medical Imaging
Abstract: Cell migration is a key feature for living organisms. Image analysis tools are useful in studying cell migration in three-dimensional (3-D) in vitro environments. We consider angiogenic vessels formed in 3-D microfluidic devices (MFDs) and develop an image analysis system to extract cell behaviors from experimental phase-contrast microscopy image sequences. The proposed system initializes tracks with the end-point confocal nuclei coordinates. We apply convolutional neural networks to detect cell candidates and combine backward Kalman filtering with multiple hypothesis tracking to link the cell candidates at each time step. These hypotheses incorporate prior knowledge on vessel formation and cell proliferation rates. The association accuracy reaches 86.4% for the proposed algorithm, indicating that the proposed system is able to associate cells more accurately than existing approaches. Cell culture experiments in 3-D MFDs have shown considerable promise for improving biology research. The proposed system is expected to be a useful quantitative tool for potential microscopy problems of MFDs.
URI: https://hdl.handle.net/10356/87775
http://hdl.handle.net/10220/45505
ISSN: 2329-4302
DOI: http://dx.doi.org/10.1117/1.JMI.5.2.024005
Rights: © 2018 The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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