Abstract
In this paper, we present an algorithm to create 3D segmentations of neuronal cells from stacks of previously segmented 2D images. The idea behind this proposal is to provide a general method to reconstruct 3D structures from 2D stacks, regardless of how these 2D stacks have been obtained. The algorithm not only reuses the information obtained in the 2D segmentation, but also attempts to correct some typical mistakes made by the 2D segmentation algorithms (for example, under segmentation of tightly-coupled clusters of cells). We have tested our algorithm in a real scenario—the segmentation of the neuronal nuclei in different layers of the rat cerebral cortex. Several representative images from different layers of the cerebral cortex have been considered and several 2D segmentation algorithms have been compared. Furthermore, the algorithm has also been compared with the traditional 3D Watershed algorithm and the results obtained here show better performance in terms of correctly identified neuronal nuclei.
Highlights
The development of methods that accurately estimate the number of cells in the brain is a major challenge in neuroscience
We have tested our algorithm in a real scenario—the segmentation of the neuronal nuclei in different layers of the rat cerebral cortex
We have focused on counting neuronal cells located in the rat neocortex, which is a multi-laminated and highly organized structure with different cell densities in different layers, making it ideal for testing the reliability of the method in different conditions
Summary
The development of methods that accurately estimate the number of cells (neurons and glia) in the brain is a major challenge in neuroscience. Numerous methods have been developed to estimate the number of cells in a given volume of brain tissue [e.g., stereology Sterio (1984); Williams and Rakic (1988); West and Gundersen (1990)]. Most of the methods for neuron counting are based on manual detection and are time consuming and userdependent. The automated segmentation of neurons would be a more efficient and unbiased alternative. The development of efficient and automatic methods to determine the actual number of cells is a major aim in neuroanatomy
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
