Quantitative analysis of neuronal morphologies usually begins with choosing a particular feature representation in order to make individual morphologies amenable to standard statistics tools and machine learning algorithms. Many different feature representations have been suggested in the literature, ranging from density maps to intersection profiles, but they have never been compared side by side. Here we performed a systematic comparison of various representations, measuring how well they were able to capture the difference between known morphological cell types. For our benchmarking effort, we used several curated data sets consisting of mouse retinal bipolar cells and cortical inhibitory neurons. We found that the best performing feature representations were two-dimensional density maps, two-dimensional persistence images and morphometric statistics, which continued to perform well even when neurons were only partially traced. Combining these feature representations together led to further performance increases suggesting that they captured non-redundant information. The same representations performed well in an unsupervised setting, implying that they can be suitable for dimensionality reduction or clustering.

中文翻译：

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对细胞类型区分的中间神经元形态学表示的系统评价。

神经元形态的定量分析通常从选择特定的特征表示开始，以使单个形态适合于标准统计工具和机器学习算法。从密度贴图到相交轮廓，文献中已经提出了许多不同的特征表示，但从未将它们并排比较。在这里，我们对各种表示形式进行了系统的比较，测量了它们能够捕获已知形态细胞类型之间差异的程度。为了进行基准测试，我们使用了几种精选的数据集，其中包括小鼠视网膜双极细胞和皮层抑制性神经元。我们发现，表现最佳的特征表示是二维密度图，二维余辉图像和形态计量统计数据，即使仅部分追踪神经元，其性能仍然很好。将这些功能表示组合在一起可进一步提高性能，这表明它们捕获了非冗余信息。相同的表示在无监督的情况下表现良好，这意味着它们可以适合于降维或聚类。