Journal
NEUROINFORMATICS
Volume 18, Issue 4, Pages 591-609Publisher
HUMANA PRESS INC
DOI: 10.1007/s12021-020-09461-z
Keywords
Neuroanatomy; Benchmarking; Cell types; Mouse; Visual cortex
Funding
- Projekt DEAL - German Ministry of Education and Research [FKZ 01GQ1601]
- German Research Foundation (DFG) under Germany's Excellence Strategy [EXC 2064/1 - 390727645, BE5601/4-1, SFB 1233, 276693517]
- National Institute of Mental Health [U19MH114830]
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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.
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