Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging.
Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging.
复制标题
卷积神经网络在双光子活体成像数据处理中的高效实现。
DOI：
10.1093/bioinformatics/btz055
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发表时间：
2019
期刊：
影响因子：
5.8
通讯作者：
Chen Zhang
中科院分区：
文献类型：
--
作者：
Wang Yangzhen;Su Feng;Wang Shanshan;Yang Chaojuan;Tian Yonglu;Yuan Peijiang;Liu Xiaorong;Xiong Wei;Chen Zhang
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 implementationImageCN is implemented in MATLAB. The source code and documentation are available at https://github.com/ZhangChenLab/ImageCN.Supplementary informationSupplementary data are available atBioinformaticsonline.
