Name
Abstract | ||
|---|---|---|
The digital reconstruction of neuronal morphology from single neurons, also called neuron tracing, is a crucial process to gain a better understanding of the relationship and connections in neuronal networks. However, the fully automation of neuron tracing remains a big challenge due to the biological diversity of the neuronal morphology, varying image qualities captured by different microscopes and large-scale nature of neuron image datasets. A common phenomenon in the low quality neuron images is the broken structures. To tackle this problem, we propose a novel automatic 3D neuron reconstruction framework named exhaustive tracing including distance transform, optimally oriented flux filter, fast-marching and hierarchical pruning. The proposed exhaustive tracing algorithm shows a robust capability of striding over large gaps in the low quality neuron images. It outperforms state-of-the-art neuron tracing algorithms by evaluating the tracing results on the large-scale First-2000 dataset and Gold dataset. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICCVW.2017.23 | 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) |
Keywords | Field | DocType |
automatic 3D single neuron reconstruction,digital reconstruction,neuronal morphology,single neurons,neuron tracing,neuronal networks,neuron image datasets,exhaustive tracing algorithm,low-quality neuron images | Iterative reconstruction,Computer vision,Pattern recognition,Computer science,Automation,Distance transform,Artificial intelligence,Digital reconstruction,Neuron,Tracing | Conference |
Volume | Issue | ISSN |
2017 | 1 | 2473-9936 |
ISBN | Citations | PageRank |
978-1-5386-1035-0 | 0 | 0.34 |
References | Authors | |
16 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zihao Tang | 1 | 1 | 1.70 |
| Donghao Zhang | 2 | 36 | 8.73 |
| Siqi Liu | 3 | 108 | 15.57 |
| Yang Song | 4 | 379 | 53.25 |
| Hanchuan Peng | 5 | 3930 | 182.27 |
| Weidong Cai | 6 | 74 | 8.50 |