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Poster
Automated scalable segmentation of neurons from multispectral images
Uygar Sümbül · Douglas Roossien · Dawen Cai · Fei Chen · Nicholas Barry · John Cunningham · Edward Boyden · Liam Paninski

Mon Dec 05 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #167
in Monday Posters »

Reconstruction of neuroanatomy is a fundamental problem in neuroscience. Stochastic expression of colors in individual cells is a promising tool, although its use in the nervous system has been limited due to various sources of variability in expression. Moreover, the intermingled anatomy of neuronal trees is challenging for existing segmentation algorithms. Here, we propose a method to automate the segmentation of neurons in such (potentially pseudo-colored) images. The method uses spatio-color relations between the voxels, generates supervoxels to reduce the problem size by four orders of magnitude before the final segmentation, and is parallelizable over the supervoxels. To quantify performance and gain insight, we generate simulated images, where the noise level and characteristics, the density of expression, and the number of fluorophore types are variable. We also present segmentations of real Brainbow images of the mouse hippocampus, which reveal many of the dendritic segments.

Keywords: Clustering · (Application) Computer Vision · (Application) Object and Pattern Recognition · (Other) Neuroscience
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Author Information

Uygar Sümbül (Columbia University)
Douglas Roossien (University of Michigan)
Dawen Cai (University of Michigan)
Fei Chen (Massachusetts Institute of Technology)
Nicholas Barry (Massachusetts Institute of Technology)
John Cunningham (Columbia University)
Edward Boyden (Massachusetts Institute of Technology)
Liam Paninski (Columbia University)

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