HDCGUnet:用于钙成像图像分割的神经网络  

HDCGUnet:a neural network for image segmentation of calcium imaging

作　　者：夏文龙 吴燕 赵哲 范明 吴海涛 XIA Wenlong;WU Yan;ZHAO Zhe;FAN Ming;WU Haitao(School of Information Science and Engineering,Lanzhou University,Lanzhou 730000,China;Institute of Military Cognition and Brain Sciences,Academy of Military Medical Sciences,Academy of Military Sciences,Beijing 100850,China)

机构地区：[1]兰州大学信息科学与工程学院,兰州730000 [2]军事科学院军事医学研究院军事认知与脑科学研究所,北京100850

出　　处：《军事医学》2024年第2期122-128,共7页Military Medical Sciences

基　　金：国家自然科学基金项目(32171148,32325025);科技创新2030-脑科学与类脑研究重大项目(2021ZD0202500);科技部重点研发专项(2021YFA1101801)。

摘　　要：目的基于Unet基础架构进行改进,探索构建一种适用于对二维钙成像荧光图像进行识别分割的神经网络。方法利用微型化双光子显微镜(mTPM)对自由移动小鼠脑区进行成像,使用NoRMCorre算法对成像数据进行运动校正,校正后使用ImageJ处理成像数据获得原始图像,并使用Labelme制作标签。搭建神经网络HDCGUnet,使用原始图像和标签进行训练,根据训练效果优化改进模型结构,并选取评价指标和其他模型进行比较,验证模型效果。结果HDCGUnet模型在自行采集制作的双光子钙成像数据集中表现最佳,并在BBBC数据集上表现良好。结论HDCGUnet为双光子钙成像图像的识别与分割提供了一种新的选择。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.

关 键 词：双光子 钙成像 细胞分割 神经网络 

分 类 号：R338[医药卫生—人体生理学] TP391.41[医药卫生—基础医学]