Name
Abstract | ||
|---|---|---|
3D reconstruction of neuronal morphology is crucial to solving neuron-related problems in neuroscience as it is a key technique for investigating the connectivity and functionality of the neuron system. Many methods have been proposed to improve the accuracy of digital neuron reconstruction. However, the large amount of computer memory and computation time they require to process the large-scale images have posed a new challenge for us. To solve this problem, we introduce a novel Memory (and Time) Efficient Image Tracing (MEIT) framework. Evaluated on the Gold dataset, our proposed method achieves better or competitive performance compared to state-of-the-art neuron tracing methods in most cases while requiring less memory and time. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/DICTA.2018.8615765 | 2018 Digital Image Computing: Techniques and Applications (DICTA) |
Keywords | Field | DocType |
neuron tracing,neuron morphology | Computer vision,Pattern recognition,Computer science,Artificial intelligence,Computer memory,Tracing,3D reconstruction,Computation | Conference |
ISBN | Citations | PageRank |
978-1-5386-6603-6 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heng Wang | 1 | 2792 | 82.10 |
| Donghao Zhang | 2 | 36 | 8.73 |
| Yang Song | 3 | 379 | 53.25 |
| Siqi Liu | 4 | 108 | 15.57 |
| Rong Gao | 5 | 68 | 13.41 |
| Hanchuan Peng | 6 | 3930 | 182.27 |
| Weidong Cai | 7 | 938 | 86.65 |