We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing "curve" skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.

异常值检测是指偏离一般数据分布的数据点的识别。现有的无监督方法经常遭受高计算成本，复杂的绰号调谐以及有限的解释性，特别是在使用大型高维数据集时。为了解决这些问题，我们介绍了一种称为ECOD（基于实证累积分布的异常值检测）的简单而有效的算法，这是由异常值常常出现在分布尾部的“罕见事件”的事实的启发。在简而言之，ECOD首先通过计算数据的各维度的经验累积分布来估计输入数据的基础分布以非参数。 ECOD然后使用这些经验分布来估计每个数据点的每维的尾部概率。最后，ECOD通过跨尺寸聚合估计的尾概率来计算每个数据点的异常值。我们的贡献如下：（1）我们提出了一种名为ECOD的新型异常检测方法，这既是可参数又易于解释; （2）我们在30个基准数据集上进行广泛的实验，在那里我们发现ECOD在准确性，效率和可扩展性方面优于11个最先进的基线; （3）我们释放易于使用和可扩展的（具有分布式支持）Python实现，以实现可访问性和再现性。