Name
Abstract | ||
|---|---|---|
We address the problem of automatic 3D segmentation of a stack of electron microscopy sections of brain tissue. Unlike previous efforts, where the reconstruction is usually done on a section-to-section basis, or by the agglomerative clustering of 2D segments, we leverage information from the entire volume to obtain a globally optimal 3D segmentation. To do this, we formulate the segmentation as the solution to a fusion problem. We first enumerate multiple possible 2D segmentations for each section in the stack, and a set of 3D links that may connect segments across consecutive sections. We then identify the fusion of segments and links that provide the most globally consistent segmentation of the stack. We show that this two-step approach of pre-enumeration and posterior fusion yields significant advantages and provides state-of-the-art reconstruction results. Finally, as part of this method, we also introduce a robust rotationally-invariant set of features that we use to learn and enumerate the above 2D segmentations. Our features outperform previous connectomic-specific descriptors without relying on a large set of heuristics or manually designed filter banks. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICCV.2011.6126240 | Computer Vision |
Keywords | Field | DocType |
biological tissues,brain,electron microscopy,image fusion,image reconstruction,image segmentation,medical image processing,2D segmentation,3D link,agglomerative clustering,automatic 3D segmentation,brain tissue,connectomic-specific descriptor,electron microscopy stack,robust rotationally-invariant feature set,section-to-section basis,segment fusion,segmentation fusion,state-of-the-art reconstruction,two-step approach | Iterative reconstruction,Hierarchical clustering,Computer vision,Connectomics,Scale-space segmentation,Image fusion,Pattern recognition,Segmentation,Computer science,Image segmentation,Heuristics,Artificial intelligence | Conference |
Volume | Issue | ISSN |
2011 | 1 | 1550-5499 |
ISBN | Citations | PageRank |
978-1-4577-1101-5 | 33 | 1.65 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vazquez-Reina, A. | 1 | 33 | 1.65 |
| Gelbart, M. | 2 | 33 | 1.65 |
| Huang, D. | 3 | 76 | 9.81 |
| Jeff W. Lichtman | 4 | 134 | 12.41 |