SSBD:database

Detail of 140911_DiR_x1_worm3

Yu Toyoshima, Terumasa Tokunaga, Osamu Hirose, Manami Kanamori, Takayuki Teramoto, Moon Sun Jang, Sayuri Kuge, Takeshi Ishihara, Ryo Yoshida, Yuichi Iino (2016) Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space., PLoS computational biology, Volume 12, Number 6, pp. e1004970

Published in 2016 Jun (Electronic publication in June 6, 2016, midnight )

• doi: 10.1371/journal.pcbi.1004970
• pmid: 27271939
  
• url: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004970
  

(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.

• Animals
• Caenorhabditis elegans/cytology
• Caenorhabditis elegans/physiology
• Cell Nucleus/physiology[major]
• Computational Biology
• Databases, Factual
• Imaging, Three-Dimensional/methods[major]
• Normal Distribution
• Pattern Recognition, Automated/methods[major]