3D Segmentation of Neuronal Nuclei and Cell-Type Identification using Multi-channel Information
<|reference_start|>3D Segmentation of Neuronal Nuclei and Cell-Type Identification using Multi-channel Information: Background Analyzing images to accurately estimate the number of different cell types in the brain using automatic methods is a major objective in neuroscience. The automatic and selective detection and segmentation of neurons would be an important step in neuroanatomical studies. New method We present a method to improve the 3D reconstruction of neuronal nuclei that allows their segmentation, excluding the nuclei of non-neuronal cell types. Results We have tested the algorithm on stacks of images from rat neocortex, in a complex scenario (large stacks of images, uneven staining, and three different channels to visualize different cellular markers). It was able to provide a good identification ratio of neuronal nuclei and a 3D segmentation. Comparison with Existing Methods: Many automatic tools are in fact currently available, but different methods yield different cell count estimations, even in the same brain regions, due to differences in the labeling and imaging techniques, as well as in the algorithms used to detect cells. Moreover, some of the available automated software methods have provided estimations of cell numbers that have been reported to be inaccurate or inconsistent after evaluation by neuroanatomists. Conclusions It is critical to have a tool for automatic segmentation that allows discrimination between neurons, glial cells and perivascular cells. It would greatly speed up a task that is currently performed manually and would allow the cell counting to be systematic, avoiding human bias. Furthermore, the resulting 3D reconstructions of different cell types can be used to generate models of the spatial distribution of cells.<|reference_end|>
@article{latorre20243d,
    title={3D Segmentation of Neuronal Nuclei and Cell-Type Identification using
  Multi-channel Information},
    author={Antonio LaTorre, Lidia Alonso-Nanclares, Jos'e Mar'ia Pe~na, Javier
  De Felipe},
    journal={Expert Systems with Applications, vol. 183, p. 115443, Nov. 2021},
    year={2024},
    doi={10.1016/j.eswa.2021.115443},
    archivePrefix={arXiv},
    eprint={2410.03248},
    primaryClass={eess.IV cs.CV}
}