Automated Delineation of Dendritic Networks in Noisy Image Stacks

We present a novel approach to 3D delineation of dendritic networks in noisy image stacks. We achieve a level of automation beyond that of state-of-the-art systems, which model dendrites as continuous tubular structures and postulate simple appearance models. Instead, we learn models from the data itself, which make them better suited to handle noise and deviations from expected appearance. From very little expert-labeled ground truth, we train both a classifier to recognize individual dendrite voxels and a density model to classify segments connecting pairs of points as dendrite-like or not. Given these models, we can then trace the dendritic trees of neurons automatically by enforcing the tree structure of the resulting graph. We will show that our approach performs better than traditional techniques on brighfield image stacks.


Editor(s):
Forsyth, David
Torr, Phillip
Zisserman, Andrew
Published in:
Proceedings of the 10th European Conference on Computer Vision, 214-227
Presented at:
European Conference on Computer Vision, Marseille, October 13-16, 2008
Year:
2008
Publisher:
Berlin, Springer-Verlag
Keywords:
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 Record created 2008-09-15, last modified 2018-09-13

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