Name
Abstract | ||
|---|---|---|
Tracing of neuron paths is important in neuroscience. Recent studies have shown that it is possible to segment and reconstruct three-dimensional morphology of axons and dendrites using fully automatic neuron tracing methods. A specific tracer may be better than others for a specific dataset, but another tracer could perform better for some other datasets. Ensemble of learners is an effective way to improve learning accuracy in machine learning. We developed automatic ensemble neuron tracers, which consistently perform well on 57 datasets of 5 species collected from 7 laboratories worldwide. Quantitative evaluation based on the data generated by human annotators shows that the proposed ensemble tracers are valuable for 3D neuron tracing and can be widely applied to different datasets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s12021-017-9325-1 | Neuroinformatics |
Keywords | Field | DocType |
3D neuron reconstruction,Ensemble neuron tracer | TRACER,Computer science,Artificial intelligence,Neuron,Tracing,Machine learning | Journal |
Volume | Issue | ISSN |
15 | 2 | 1559-0089 |
Citations | PageRank | References |
3 | 0.38 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ching-Wei Wang | 1 | 177 | 15.80 |
| Yu-Ching Lee | 2 | 3 | 0.38 |
| Hilmil Pradana | 3 | 3 | 0.38 |
| Zhi Zhou | 4 | 152 | 10.11 |
| Hanchuan Peng | 5 | 3930 | 182.27 |