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Updated: Apr 28, 2026
Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations
Published on: December 16, 2017
Automated detection and tracking of many cells by using 4D live-cell imaging data
Terumasa Tokunaga1, Osamu Hirose1, Shotaro Kawaguchi2
1The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN.
Motivation:
Automated fluorescence microscopes produce massive amounts of images observing cells, often in four dimensions of space and time. This study addresses two tasks of time-lapse imaging analyses; detection and tracking of the many imaged cells, and it is especially intended for 4D live-cell imaging of neuronal nuclei of Caenorhabditis elegans. The cells of interest appear as slightly deformed ellipsoidal forms. They are densely distributed, and move rapidly in a series of 3D images. Thus, existing tracking methods often fail because more than one tracker will follow the same target or a tracker transits from one to other of different targets during rapid moves.
Results:
The present method begins by performing the kernel density estimation in order to convert each 3D image into a smooth, continuous function. The cell bodies in the image are assumed to lie in the regions near the multiple local maxima of the density function. The tasks of detecting and tracking the cells are then addressed with two hill-climbing algorithms. The positions of the trackers are initialized by applying the cell-detection method to an image in the first frame. The tracking method keeps attacking them to near the local maxima in each subsequent image. To prevent the tracker from following multiple cells, we use a Markov random field (MRF) to model the spatial and temporal covariation of the cells and to maximize the image forces and the MRF-induced constraint on the trackers. The tracking procedure is demonstrated with dynamic 3D images that each contain >100 neurons of C.elegans.
Availability:
http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014 SUPPLEMENTARY INFORMATION: Supplementary data are available at http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014
