Paper Info
Title
Automated axon tracking of 3D confocal laser scanning microscopy images using guided probabilistic region merging.
Abstract
This paper presents a new algorithm for extracting the centerlines of the axons from a 3D data stack collected by a confocal laser scanning microscope. Recovery of neuronal structures from such datasets is critical for quantitatively addressing a range of neurobiological questions such as the manner in which the branching pattern of motor neurons change during synapse elimination. Unfortunately, the data acquired using fluorescence microscopy contains many imaging artifacts, such as blurry boundaries and non-uniform intensities of fluorescent radiation. This makes the centerline extraction difficult. We propose a robust segmentation method based on probabilistic region merging to extract the centerlines of individual axons with minimal user interaction. The 3D model of the extracted axon centerlines in three datasets is presented in this paper. The results are validated with the manual tracking results while the robustness of the algorithm is compared with the published repulsive snake algorithm.
Year
DOI
Venue
2009
10.1007/s12021-008-9038-6
Neuroinformatics
Keywords
Field
DocType
maximum intensity projection.segmentation. guided region growing.watershed
Computer vision,Fluorescence microscope,Segmentation,Computer science,Maximum intensity projection,Axon,Robustness (computer science),Artificial intelligence,Probabilistic logic,Merge (version control),Confocal laser scanning microscopy
Journal
Volume
Issue
ISSN
5
3
1539-2791
Citations 
PageRank 
References 
12
0.76
10
Authors
6
Name
Order
Citations
PageRank
Ranga Srinivasan1221.80
Xiaobo Zhou282769.95
Eric Miller3538.22
Ju Lu416611.97
Jeff W. Lichtman513412.41
Stephen T. C. Wong61081134.56