ArticleBioinformatics (Oxford, England)2019
Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- High-throughput markerless pose estimation and home-cage activity analysis of tree shrew using deep learning.Animal models and experimental medicine · 2025Article
- Cable-free brain imaging for multiple free-moving animals with miniature wireless microscopes.Journal of biomedical optics · 2023Article
- Restoration of FMRP expression in adult V1 neurons rescues visual deficits in a mouse model of fragile X syndrome.Protein & cell · 2022Article
- Biomimetic Materials and Their Utility in Modeling the 3-Dimensional Neural Environment.iScience · 2020Review
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Authors and funding
9 authors.
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Abstract
motivationFunctional imaging at single-neuron resolution offers a highly efficient tool for studying the functional connectomics in the brain. However, mainstream neuron-detection methods focus on either the morphologies or activities of neurons, which may lead to the extraction of incomplete information and which may heavily rely on the experience of the experimenters.
resultsWe developed a convolutional neural networks and fluctuation method-based toolbox (ImageCN) to increase the processing power of calcium imaging data. To evaluate the performance of ImageCN, nine different imaging datasets were recorded from awake mouse brains. ImageCN demonstrated superior neuron-detection performance when compared with other algorithms. Furthermore, ImageCN does not require sophisticated training for users. AVAILABILITY AND IMPLEMENTATION: ImageCN is implemented in MATLAB. The source code and documentation are available at https://github.com/ZhangChenLab/ImageCN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.