Name
Abstract | ||
|---|---|---|
The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed from isotropic (e.g., FIBSEM) or near isotropic (e.g., SBEM) data. In spite of the remarkable progress on algorithms in recent years, automatic dense reconstruction from anisotropic data remains a challenge for the connectomics community. One significant hurdle in the segmentation of anisotropic data is the difficulty in generating a suitable initial over-segmentation. In this study, we present a segmentation method for anisotropic EM data that agglomerates a 3D over-segmentation computed from the 3D affinity prediction. A 3D U-net is trained to predict 3D affinities by the MALIS approach. Experiments on multiple datasets demonstrates the strength and robustness of the proposed method for anisotropic EM segmentation. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Isotropy,Connectomics,Economies of agglomeration,Anisotropy,Pattern recognition,Segmentation,Computer science,Robustness (computer science),Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1707.08935 | 0 |
PageRank | References | Authors |
0.34 | 12 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Toufiq Parag | 1 | 52 | 7.18 |
| Fabian Tschopp | 2 | 3 | 0.73 |
| William Grisaitis | 3 | 3 | 0.73 |
| Srinivas C. Turaga | 4 | 127 | 23.75 |
| Xuewen Zhang | 5 | 0 | 1.35 |
| Brian Matejek | 6 | 12 | 3.54 |
| Lee Kamentsky | 7 | 26 | 3.23 |
| Jeff W. Lichtman | 8 | 134 | 12.41 |
| Hanspeter Pfister | 9 | 5933 | 340.59 |