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Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
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Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
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Automated bone cell classification for confocal laser scanning microscopy volumes.

Brennan T Flannery1,2, Xiaoyu Xu2, Brian T Golz2

  • 1Case Western Reserve University, Department of Biomedical Engineering, 10900 Euclid Ave, Cleveland, OH 44106, USA.

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Summary

This study presents an automated algorithm for classifying bone cells in 3D microscopy images, significantly reducing analysis time and improving accuracy compared to manual methods. The developed tool offers a fast, repeatable, and objective approach for cell identification in fluorescence imaging.

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Area of Science:

  • Biomedical Engineering
  • Cell Biology
  • Microscopy Imaging

Background:

  • Manual cell classification in microscopy is subjective and time-consuming.
  • Identifying bone cells is challenging due to irregular shapes and bone mineral presence.
  • Fluorescence-based lineage tracing increases subjectivity in cell classification.

Purpose of the Study:

  • To develop and validate an automated algorithm for objective cell identification and classification in 3D bone cell cultures.
  • To classify osteoblast-lineage cells based on cell body color in fluorescence microscopy images.
  • To compare the automated algorithm's performance against manual labeling.

Main Methods:

  • Developed a MATLAB-based automated algorithm for cell identification and classification.
  • Utilized flattened volumetric image stacks from 3D bone cell cultures.
  • Validated the algorithm by comparing its output to manual labeling on eleven images.

Main Results:

  • The automated algorithm achieved precision, recall, and F1 scores higher than 0.75 for all cell classifications, with most exceeding 0.80.
  • No significant differences were observed between manual and automated cell counts or classifications.
  • Automated analysis averaged 17 seconds per image, compared to over 10 minutes for manual labeling.

Conclusions:

  • The developed algorithm provides a fast, repeatable, and accurate method for classifying bone cells using fluorescence in confocal microscopy.
  • This automated process can be applied to other pre-clinical models and pathological tissues for cell identification.
  • The algorithm objectively addresses the limitations of manual cell classification in complex biological samples.