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PLoS Comput Biol. 2016 Jun 6;12(6):e1004970. doi: 10.1371/journal.pcbi.1004970. eCollection 2016 Jun.

Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space.

Author information

1
Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
2
Department of Systems Design and Informatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka-shi, Fukuoka, Japan.
3
The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan.
4
Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University, Kakuma, Kanazawa, Japan.
5
Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan.
6
CREST, Japan Science and Technology Corporation, Bunkyo-ku, Tokyo, Japan.

Abstract

To measure the activity of neurons using whole-brain activity imaging, precise detection of each neuron or its nucleus is required. In the head region of the nematode C. elegans, the neuronal cell bodies are distributed densely in three-dimensional (3D) space. However, no existing computational methods of image analysis can separate them with sufficient accuracy. Here we propose a highly accurate segmentation method based on the curvatures of the iso-intensity surfaces. To obtain accurate positions of nuclei, we also developed a new procedure for least squares fitting with a Gaussian mixture model. Combining these methods enables accurate detection of densely distributed cell nuclei in a 3D space. The proposed method was implemented as a graphical user interface program that allows visualization and correction of the results of automatic detection. Additionally, the proposed method was applied to time-lapse 3D calcium imaging data, and most of the nuclei in the images were successfully tracked and measured.

PMID:
27271939
PMCID:
PMC4894571
DOI:
10.1371/journal.pcbi.1004970
[Indexed for MEDLINE]
Free PMC Article

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