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.
Highlights
The animal brain is the most complex information processing system in living organisms
We focused on the head neurons of the soil nematode C. elegans, which constitute the major neuronal ensemble of this animal [21]
The distance to the nearest neighboring nucleus was 4.30 ± 2.13 μm, suggesting that the neurons are densely distributed in 3D space
Summary
The animal brain is the most complex information processing system in living organisms. The wiring information for neural circuits and visualization of their activity at cellular resolution are required for achieving this goal. Advances in microscopy techniques in recent years have enabled whole-brain activity imaging of small animals at cellular resolution [1,2,3,4]. The wiring information of all the neurons in the mouse brain can be obtained using recently developed brain-transparentization techniques [5,6,7,8,9]. Detection of neurons from microscopy images is necessary for optical measurements of neuronal activity or for obtaining wiring information. Because there are many neurons in the images, methods of automatic neuron detection, rather than manual selection of ROIs (regions of interest), are required and several such methods have been proposed [10,11]. Detection of cells that are distributed in three-dimensional (3D) space is important in other fields of biology such as embryonic development studies [12,13,14,15,16,17]
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