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An automated three-dimensional detection and segmentation method for touching cells by integrating concave points

Yong He1, Yunlong Meng1, Hui Gong1

  • 1Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.

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Summary

This study introduces an automated method for 3D cell detection and segmentation in mouse brains using Nissl staining. The approach accurately segments touching cells, improving cytoarchitecture analysis for neuroscience research.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Biomedical Imaging

Background:

  • Understanding brain cytoarchitecture is vital for neuroscience and studying neural diseases.
  • Accurate cell population centroid and contour extraction are key neuroanatomical tasks.
  • Nissl staining enables single-cell resolution imaging of entire mouse brains, but segmentation of touching cells remains challenging.

Purpose of the Study:

  • To develop an automated 3D detection and segmentation method for Nissl-stained mouse brain data.
  • To address the challenge of precisely segmenting numerous, closely touching cells.
  • To improve the accuracy and robustness of cell cytoarchitecture characterization.

Main Methods:

  • Developed a two-step automated method for 3D cell detection and segmentation.
  • Utilized concave points clustering to identify seed points for touching cells.
  • Employed random walker segmentation to delineate individual cell contours.

Main Results:

  • Successfully applied the method to micro-optical sectioning tomography (MOST) datasets of mouse brains.
  • Demonstrated effective segmentation of closely touching cells.
  • Achieved promising detection accuracy and high robustness compared to traditional methods.

Conclusions:

  • The proposed automated method offers a significant advancement in 3D cell segmentation for Nissl-stained brain imaging.
  • This technique enhances the ability to characterize cytoarchitecture, aiding in the understanding of brain function and disease.
  • The approach provides a robust tool for neuroanatomical analysis, particularly in complex datasets with numerous adjacent cells.