Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space.
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.
Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
Yu
0000-0001-5239-2235
Y
Toyoshima
Department of Systems Design and Informatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka-shi, Fukuoka, Japan.|The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan.
Terumasa
T
Tokunaga
Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University, Kakuma, Kanazawa, Japan.
Osamu
0000-0002-8077-8589
O
Hirose
Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
Manami
M
Kanamori
Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan.
Takayuki
T
Teramoto
Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
Moon Sun
MS
Jang
Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan.
Sayuri
S
Kuge
Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan.
Takeshi
T
Ishihara
The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan.
Ryo
R
Yoshida
Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.|CREST, Japan Science and Technology Corporation, Bunkyo-ku, Tokyo, Japan.
Yuichi
Y
Iino
D000818:Animals; D017173:Caenorhabditis elegans / Q000166:cytology / Q000502:physiology; D002467:Cell Nucleus / Q000502:physiology*; D019295:Computational Biology; D016208:Databases, Factual; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*