Xu, Zhehao and Wu, Yukun and Guan, Jiangheng and Liang, Shanshan and Pan, Junxia and Wang, Meng and Hu, Qianshuo and Jia, Hongbo and Chen, Xiaowei and Liao, Xiang (2023) NeuroSeg-II: A deep learning approach for generalized neuron segmentation in two-photon Ca2+ imaging. Frontiers in Cellular Neuroscience, 17. ISSN 1662-5102
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Abstract
The development of two-photon microscopy and Ca2+ indicators has enabled the recording of multiscale neuronal activities in vivo and thus advanced the understanding of brain functions. However, it is challenging to perform automatic, accurate, and generalized neuron segmentation when processing a large amount of imaging data. Here, we propose a novel deep-learning-based neural network, termed as NeuroSeg-II, to conduct automatic neuron segmentation for in vivo two-photon Ca2+ imaging data. This network architecture is based on Mask region-based convolutional neural network (R-CNN) but has enhancements of an attention mechanism and modified feature hierarchy modules. We added an attention mechanism module to focus the computation on neuron regions in imaging data. We also enhanced the feature hierarchy to extract feature information at diverse levels. To incorporate both spatial and temporal information in our data processing, we fused the images from average projection and correlation map extracting the temporal information of active neurons, and the integrated information was expressed as two-dimensional (2D) images. To achieve a generalized neuron segmentation, we conducted a hybrid learning strategy by training our model with imaging data from different labs, including multiscale data with different Ca2+ indicators. The results showed that our approach achieved promising segmentation performance across different imaging scales and Ca2+ indicators, even including the challenging data of large field-of-view mesoscopic images. By comparing state-of-the-art neuron segmentation methods for two-photon Ca2+ imaging data, we showed that our approach achieved the highest accuracy with a publicly available dataset. Thus, NeuroSeg-II enables good segmentation accuracy and a convenient training and testing process.
Item Type: | Article |
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Subjects: | AP Academic Press > Medical Science |
Depositing User: | Unnamed user with email support@apacademicpress.com |
Date Deposited: | 24 May 2023 05:44 |
Last Modified: | 06 Sep 2024 07:54 |
URI: | http://info.openarchivespress.com/id/eprint/1365 |