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.
Author Summary To reach the ultimate goal of neuroscience to understanding how each neuron functions in the brain, whole-brain activity imaging techniques with single-cell resolution have been intensively developed. There are many neurons in the whole-brain images and manual detection of the neurons is very time-consuming. However, the neurons are often packed densely in the 3D space and existing automatic methods fail to correctly split the clumps. In fact, in previous reports of whole-brain activity imaging of C. elegans, the number of detected neurons were less than expected. Such scarcity may be a cause of measurement errors and misidentification of neuron classes. Here we developed a highly accurate automatic cell detection method for densely-packed cells. The proposed method successfully detected almost all neurons in whole-brain images of the nematode. Our method can be used to track multi-objects and enables automatic measurements of the neuronal activities from whole-brain activity imaging data. We also developed a visualization and correction tool that is helpful for experimenters. Additionally, the proposed method can be a fundamental technique for other applications such as making wiring diagram of neurons or establishing a cell lineage in embryonic development. Thus our framework supports effective and accurate bio-image analyses.