Title
Robust 3D reconstruction and mean-shift clustering of motoneurons from serial histological images
Abstract
Motoneurons (MNs) are neuronal cells involved in several central nervous system (CNS) diseases. In order to develop new treatments and therapies, there is a need to understand MN organization and differentiation. Although recently developed embryo mouse models have enabled the investigation of the MN specialization process, more robust and reproducible methods are required to evaluate the topology and structure of the neuron bundles. In this article, we propose a new fully automatic approach to identify MN clusters from stained histological slices. We developed a specific workflow including inter-slice intensity normalization and slice registration for 3D volume reconstruction, which enables the segmentation, mapping and 3D visualization of MN bundles. Such tools will facilitate the understanding of MN organization, differentiation and function.
Year
DOI
Venue
2010
10.1007/978-3-642-15699-1_20
MIAR
Keywords
Field
DocType
embryo mouse model,mn bundle,serial histological image,new treatment,neuron bundle,mn cluster,central nervous system,mn organization,inter-slice intensity normalization,mean-shift clustering,automatic approach,mn specialization process,3d reconstruction,mean shift,3d visualization,embryos
Neuroscience,Anatomy,Normalization (statistics),Visualization,Segmentation,Computer science,Motor pool,Mean-shift,Volume reconstruction,3D reconstruction
Conference
Volume
ISSN
ISBN
6326
0302-9743
3-642-15698-3
Citations 
PageRank 
References 
0
0.34
8
Authors
5
Name
Order
Citations
PageRank
nicolas guizard11025.89
P Coupe224610.79
Nicolas Stifani300.34
Stefano Stifani400.34
D. Louis Collins53915403.90