Mr. Yunlong Meng Profile

Yunlong Meng

at Chinese University of Hong Kong

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Proceedings Article | 17 September 2014 Paper

Automatic detection and quantitative analysis of cells in the mouse primary motor cortex

Yunlong Meng, Yong He, Jingpeng Wu, Shangbin Chen, Anan Li, Hui Gong

Proceedings Volume 9230, 92301E (2014) https://doi.org/10.1117/12.2068857

KEYWORDS: Brain, Image segmentation, Neuroimaging, Gaussian filters, Image compression, Image filtering, Image processing, Binary data, Quantitative analysis, 3D image processing

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Neuronal cells play very important role on metabolism regulation and mechanism control, so cell number is a fundamental determinant of brain function. Combined suitable cell-labeling approaches with recently proposed three-dimensional optical imaging techniques, whole mouse brain coronal sections can be acquired with 1-μm voxel resolution. We have developed a completely automatic pipeline to perform cell centroids detection, and provided three-dimensional quantitative information of cells in the primary motor cortex of C57BL/6 mouse. It involves four principal steps: i) preprocessing; ii) image binarization; iii) cell centroids extraction and contour segmentation; iv) laminar density estimation. Investigations on the presented method reveal promising detection accuracy in terms of recall and precision, with average recall rate 92.1% and average precision rate 86.2%. We also analyze laminar density distribution of cells from pial surface to corpus callosum from the output vectorizations of detected cell centroids in mouse primary motor cortex, and find significant cellular density distribution variations in different layers. This automatic cell centroids detection approach will be beneficial for fast cell-counting and accurate density estimation, as time-consuming and error-prone manual identification is avoided.

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