Paper
17 September 2014 Automatic detection and quantitative analysis of cells in the mouse primary motor cortex
Author Affiliations +
Proceedings Volume 9230, Twelfth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2014); 92301E (2014) https://doi.org/10.1117/12.2068857
Event: Twelfth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2014), 2014, Wuhan, China
Abstract
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.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yunlong Meng, Yong He, Jingpeng Wu, Shangbin Chen, Anan Li, and Hui Gong "Automatic detection and quantitative analysis of cells in the mouse primary motor cortex", Proc. SPIE 9230, Twelfth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2014), 92301E (17 September 2014); https://doi.org/10.1117/12.2068857
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Cited by 2 scholarly publications.
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KEYWORDS
Brain

Image segmentation

Neuroimaging

Gaussian filters

Binary data

Image compression

Image filtering

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