Name
Abstract | ||
|---|---|---|
The reconstruction of the neural network is essential in computational neuroscience. Here, we present an automatic algorithm to trace single neuron projections based on two core algorithmic ideas: a global step segmenting all neuron bodies and their projections and a local growing phase that accommodates to the nonuniform illumination and to the noise of the sample. We tested our algorithm on two 3D stacks of two-photon images acquired from a human dysplastic brain sample. The results show that the traces produced are statistically equivalent to the ground truth, according to the Friedman and Li tests. Furthermore, we found that our algorithm outperforms other state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ISBI.2016.7493274 | 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) |
Keywords | Field | DocType |
Neuron tracing,Human brain,Two-photon microscopy,Neuron projections,BigNeuron | Computer vision,Computational neuroscience,Pattern recognition,Computer science,Ground truth,Artificial intelligence,Artificial neural network,Tracing,3d image | Conference |
ISSN | Citations | PageRank |
1945-7928 | 1 | 0.35 |
References | Authors | |
2 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ludovica Acciai | 1 | 2 | 0.70 |
| irene costantini | 2 | 1 | 0.69 |
| Francesco Saverio Pavone | 3 | 1 | 1.03 |
| Valerio Conti | 4 | 1 | 0.35 |
| Renzo Guerrini | 5 | 2 | 0.70 |
| Paolo Soda | 6 | 407 | 39.44 |
| Giulio Iannello | 7 | 414 | 46.75 |