Graph-based Cell Pattern Recognition for Merging the Multi-modal Optical Microscopic Image of Neurons
Authors: Wenwei Li, Zhao Feng, Wu Chen, Zimin Dai, Xiaokang Chai, Sile An, Zhuang Guan, Wei Zhou, Hui Gong, Qingming Luo, Anan Li*
* Correspondence author: aali@hust.edu.cn

Overview
We have developed a comprehensive method for automatically integrating functional imaging and structural imaging information at the single neuron level. By inputting data from two-photon calcium imaging and structural slice imaging, the system automatically generates and displays neuronal pairing results. Nine structural slice imaging datasets used for registration and verification are provided, all acquired using the HD-fMOST system with a resolution of 0.65×0.65×2µm³; the functional imaging data comes from two-photon calcium imaging, with a resolution of 1×1µm², covering 22 imaging sites located in layers L2/3 and L5 of the visual cortex. The code for automatic neuronal matching is also provided.

Raw data:
The dataset comprises nine sets of mouse brain imaging data, all annotated for the visual cortex, with two-photon imaging sites including layers L2/3 and L5. The fMOST.tif files represent marked data blocks cut from structural slice data, with a resolution of 0.65×0.65×2µm³, corresponding to fMOST_label.tif and fMOST.swc files. The 2p folder contains standard deviation projection images from the two-photon imaging sites, along with annotated located neurons. The results folder contains the pairing result diagrams generated by the algorithm.

Codes:
Detailed information of codes in this paper can be accessed at https://github.com/Brainsmatics/CellGPR.