The DIADEM Metric: Comparing Multiple Reconstructions of the Same Neuron
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Digital reconstructions of neuronal morphology are used to study neuron function, development, and responses to various conditions. Although many measures exist to analyze differences between neurons, none is particularly suitable to compare the same arborizing structure over time (morphological change) or reconstructed by different people and/or software (morphological error). The metric introduced for the DIADEM (DIgital reconstruction of Axonal and DEndritic Morphology) Challenge quantifies the similarity between two reconstructions of the same neuron by matching the locations of bifurcations and terminations as well as their topology between the two reconstructed arbors. The DIADEM metric was specifically designed to capture the most critical aspects in automating neuronal reconstructions, and can function in feedback loops during algorithm development. During the Challenge, the metric scored the automated reconstructions of best-performing algorithms against manually traced gold standards over a representative data set collection. The metric was compared with direct quality assessments by neuronal reconstruction experts and with clocked human tracing time saved by automation. The results indicate that relevant morphological features were properly quantified in spite of subjectivity in the underlying image data and varying research goals. The DIADEM metric is freely released open source (http://diademchallenge.org) as a flexible instrument to measure morphological error or change in high-throughput reconstruction projects.
KeywordsAlgorithm Automation Axon Computational neuroanatomy Dendrite Digital tracing Morphology Optical imaging
- Brown, K. M., Barrionuevo, G., Canty, A. J., De Paola, V., Hirsch, J. A., Jefferis, G. S. X. E., et al. (2011) The DIADEM data sets: representative light microscopy images of neuronal morphology to advance automation of digital reconstructions. Neuroinformatics, doi:10.1007/s12021-010-9095-5.
- Bülow, T., Lorenz, C., Wiemker, R., & Honko, J. (2006). Point based methods for automatic bronchial tree matching and labeling. Proceedings of the SPIE, 7, 225–234.Google Scholar
- Canty, A. J. & De Paola, V. (2011) Axonal reconstructions going live. Neuroinformatics, doi:10.1007/s12021-011-9112-3.
- Cuntz, H., Forstner, F., Borst, A., & Häusser, M. (2011). The TREES toolbox – probing the basis of axonal and dendritic branching. Neuroinformatics, in press.Google Scholar
- Goldberg, J., Hamzei-Sichani, F., MacLean, J., Tamas, G., Urban, R., & Yuste, R. (2006). From dendrites to networks: optically probing the living brain slice and using principal component analysis to characterize neuronal morphology. In L. Zaborszky, F. G. Wouterlood, & J. L. Lanciego (Eds.), Neuroanatomical tract-tracing 3: Molecules, neurons, and systems (pp. 452–476). US: Springer.CrossRefGoogle Scholar
- Haug, H. (1987). Brain sizes, surfaces, and neuronal sizes of the cortex cerebri: a stereological investigation of man and his variability and a comparison with some mammals (primates, whales, marsupials, insectivores, and one elephant). American Journal of Anatomy, 180, 126–142.PubMedCrossRefGoogle Scholar
- Jaeger, D. (2001) Accurate reconstruction of neuronal morphology. In E. de Schutter (ed.), Computational neuroscience: Realistic modeling for experimentalists. CRC Press, pp. 159–178.Google Scholar
- Lu, J., Tapia, J. C., White, O. L., & Lichtman, J. W. (2009). The interscutularis muscle connectome. PLoS Biology, 7, e1000032.Google Scholar
- Luisi, J., Narayanaswamy, A., Galbreath, Z., & Roysam, B. (2011). The FARSIGHT Trace Editor: An Open Source Tool for 3-D Inspection and Efficient Pattern Analysis Aided Editing of Automated Neuronal Reconstructions. Neuroinformatics, doi:10.1007/s12021-011-9115-0.
- Peng, H., Long, F., Zhao, T., & Myers, E. (2011). Proof-editing is the bottleneck of 3D neuron reconstruction: the problem and solutions. Neuroinformatics, doi:10.1007/s12021-010-9090-x.
- Schaap, M., Metz, C. T., van Walsum, T., van Der Giessen, A. G., Weustink, A. C., Mollet, N. R., et al. (2009). Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Medical Image Analysis, 13, 701–714.PubMedCrossRefGoogle Scholar
- Senft, S. L. (2011). A brief history of neuronal reconstruction. Neuroinformatics , doi:10.1007/s12021-011-9107-0.