A Robust Algorithm for Segmenting and Tracking Clustered Cells in Time-Lapse Fluorescent Microscopy

We present herein a robust algorithm for cell tracking in a sequence of time-lapse 2-D fluorescent microscopy images. Tracking is performed automatically via a multiphase active contours algorithm adapted to the segmentation of clustered nuclei with obscure boundaries. An ellipse fitting method is a...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2013-07, Vol.17 (4), p.862-869
Main Authors: Tarnawski, W., Kurtcuoglu, V., Lorek, P., Bodych, M., Rotter, J., Muszkieta, M., Piwowar, L., Poulikakos, D., Majkowski, M., Ferrari, A.
Format: Article
Language:English
Subjects:
Algorithms
Cell Nucleus - physiology
Cell segmentation and tracking
Cell Tracking - methods
ellipse fitting
fluorescent microscopy
HeLa Cells
Humans
Image Processing, Computer-Assisted - methods
Microscopy, Fluorescence - methods
multiphase active contours
Time-Lapse Imaging - methods
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Description
Summary:We present herein a robust algorithm for cell tracking in a sequence of time-lapse 2-D fluorescent microscopy images. Tracking is performed automatically via a multiphase active contours algorithm adapted to the segmentation of clustered nuclei with obscure boundaries. An ellipse fitting method is applied to avoid problems typically associated with clustered, overlapping, or dying cells, and to obtain more accurate segmentation and tracking results. We provide quantitative validation of results obtained with this new algorithm by comparing them to the results obtained from the established CellProfiler, MTrack2 (plugin for Fiji), and LSetCellTracker software.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2013.2262233