Deep learning-based synapse counting and synaptic ultrastructure analysis of electron microscopy images
Deep learning-based synapse counting and synaptic ultrastructure analysis of electron microscopy images
复制标题
基于深度学习的电子显微镜图像突触计数和突触超微结构分析
DOI：
10.1016/j.jneumeth.2022.109750
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发表时间：
2022-11
期刊：
影响因子：
--
通讯作者：
Chen Zhang
中科院分区：
文献类型：
--
作者：
Feng Su;Mengping Wei;Meng Sun;Lixin Jiang;Zhaoqi Dong;Jue Wang;Chen Zhang
突触是中枢神经系统(CNS)中神经元之间的连接，或者是周围神经系统(PNS)中神经元与其他可兴奋细胞之间的连接，其中电信号或化学信号以高空间精度快速地通过一个细胞传递到另一个细胞。基于突触数目和精细形态的突触分析是了解神经功能和疾病的基础。针对人工分析电子显微镜(EM)图像中突触结构效率低、主观偏差大的问题，开发了一种基于多种深度学习模型的多功能突触分析系统。结果基于ResNet18和更快的R-CNN模型的突触计数系统平均准确率(MAP)为92.55%。对于突触超微结构的分析，基于ResNet50的快速R-CNN模型获得了91.60%的MAP，基于ResNet50的DeepLab v3+模型实现了高精度的突触前膜和突触后膜分割，全局准确率为0.9811，基于ResNet18的快速R-CNN模型获得了91.41%的突触小泡检测MAP。结论所提出的多功能突触分析系统有助于克服人工分析固有的实验偏差，从而为基于EM图像的突触功能研究提供便利。
BackgroundSynapses are the connections between neurons in the central nervous system (CNS) or between neurons and other excitable cells in the peripheral nervous system (PNS), where electrical or chemical signals rapidly travel through one cell to another with high spatial precision. Synaptic analysis, based on synapse numbers and fine morphology, is the basis for understanding neurological functions and diseases. Manual analysis of synaptic structures in electron microscopy (EM) images is often limited by low efficiency and subjective bias.New methodWe developed a multifunctional synaptic analysis system based on several advanced deep learning (DL) models. The system achieved synapse counting in low-magnification EM images and synaptic ultrastructure analysis in high-magnification EM images.ResultsThe synapse counting system based on ResNet18 and a Faster R-CNN model had a mean average precision (mAP) of 92.55%. For synaptic ultrastructure analysis, the Faster R-CNN model based on ResNet50 achieved a mAP of 91.60%, the DeepLab v3 + model based on ResNet50 enabled high performance in presynaptic and postsynaptic membrane segmentation with a global accuracy of 0.9811, and the Faster R-CNN model based on ResNet18 achieved a mAP of 91.41% for synaptic vesicle detection.ConclusionsThe proposed multifunctional synaptic analysis system may help to overcome the experimental bias inherent in manual analysis, thereby facilitating EM image–based synaptic function studies.
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