Determining the types of neurons within a nervous system plays a significant role in the analysis of brain connectomics and the investigation of neurological diseases. However, the efficiency of utilizing anatomical, physiological, or molecular characteristics of neurons is relatively low and costly. With the advancements in electron microscopy imaging and analysis techniques for brain tissue, we are able to obtain whole-brain connectome consisting neuronal high-resolution morphology and connectivity information. However, few models are built based on such data for automated neuron classification. In this paper, we propose NeuNet, a framework that combines morphological information of neurons obtained from skeleton and topological information between neurons obtained from neural circuit. Specifically, NeuNet consists of three components, namely Skeleton Encoder, Connectome Encoder, and Readout Layer. Skeleton Encoder integrates the local information of neurons in a bottom-up manner, with a one-dimensional convolution in neural skeleton's point data; Connectome Encoder uses a graph neural network to capture the topological information of neural circuit; finally, Readout Layer fuses the above two information and outputs classification results. We reprocess and release two new datasets for neuron classification task from volume electron microscopy(VEM) images of human brain cortex and Drosophila brain. Experiments on these two datasets demonstrated the effectiveness of our model with accuracy of 0.9169 and 0.9363, respectively. Code and data are available at: https://github.com/WHUminghui/NeuNet.

翻译：确定神经系统中神经元的类型在大脑连接组分析和神经疾病研究中具有重要作用。然而，利用神经元的解剖学、生理学或分子特征进行判别的效率较低且成本高昂。随着电子显微镜成像及脑组织分析技术的进步，我们能够获取包含神经元高分辨率形态和连接信息的全脑连接组。然而，目前基于此类数据构建的自动化神经元分类模型较少。本文提出NeuNet框架，该框架融合了从骨架获取的神经元形态信息与从神经回路获取的神经元间拓扑信息。具体而言，NeuNet包含三个组件：骨架编码器、连接组编码器和读出层。骨架编码器采用自底向上的方式整合神经元局部信息，利用一维卷积处理神经骨架的点数据；连接组编码器使用图神经网络捕捉神经回路的拓扑信息；最后，读出层融合上述两种信息并输出分类结果。我们重新处理并发布了两个基于人类大脑皮层与果蝇大脑体电子显微镜图像的神经元分类数据集。在两个数据集上的实验表明，本模型有效性分别达到0.9169和0.9363的准确率。代码和数据已在https://github.com/WHUminghui/NeuNet公开。