HDCGUnet:a neural network for image segmentation of calcium imaging / 军事医学

ABSTRACT

Objective To build a neural network based on the Unet infrastructure for recognition and segmentation of two-dimensional calcium imaging fluorescence images.Methods The in vivo miniaturized two-photon microscope(mTPM)was used for brain calcium imaging in freely moving mice.The imaging data was motion corrected using the NoRMCorre algorithm and processed using ImageJ software to obtain the original images after correction,and the labels were produced using the Labelme software.The neural network HDCGUnet was built using the original images and labels for training,and optimized to improve the model structure according to the training effect.Finally,the evaluation indexes were selected and compared with those of other models to verify the utility of this model.Results The HDCGUnet model,which was collected and made on our own,performed best in the two-photon calcium imaging dataset compared to other models,and performed well on the BBBC dataset either.Conclusion The HDCGUnet model provides a novel alternative for the recognition and segmentation of two-photon calcium imaging images.