Open AccessAutomated method for the segmentation and morphometry of nerve fibers in large-scale CARS images of spinal cord tissue
Author(s) -
Steve Bégin,
Olivier Dupont-Therrien,
Erik Bélanger,
Amy Daradich,
Sophie Laffray,
Yves De Koninck,
Daniel Côté
Publication year - 2014
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.5.004145
Subject(s) - segmentation , spinal cord , microscopy , artificial intelligence , computer science , image segmentation , computer vision , anatomy , image processing , nerve fiber , pattern recognition (psychology) , biomedical engineering , optics , physics , biology , medicine , image (mathematics) , neuroscience
A fully automated method for large-scale segmentation of nerve fibers from coherent anti-Stokes Raman scattering (CARS) microscopy images is presented. The method is specifically designed for CARS images of transverse cross sections of nervous tissue but is also suitable for use with standard light microscopy images. After a detailed description of the two-part segmentation algorithm, its accuracy is quantified by comparing the resulting binary images to manually segmented images. We then demonstrate the ability of our method to retrieve morphological data from CARS images of nerve tissue. Finally, we present the segmentation of a large mosaic of CARS images covering more than half the area of a mouse spinal cord cross section and show evidence of clusters of neurons with similar g-ratios throughout the spinal cord.