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OC_Finder: Osteoclast segmentation, counting, and classification using watershed and deep learning.

Xiao Wang1, Mizuho Kittaka2,3, Yilin He4

  • 1Department of Computer Science, Purdue University, West Lafayette, IN, USA.

Frontiers in Bioinformatics
|April 27, 2022
PubMed
Summary
This summary is machine-generated.

We developed OC_Finder, an automated system for identifying osteoclasts in microscopic images. This deep learning tool accurately counts tartrate-resistant acid phosphatase-positive cells, reducing manual labor and improving reproducibility in bone research.

Keywords:
Automatic SegmentationDeep learningOsteoclast countingOsteoclast segmentationopen source software

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Area of Science:

  • Cell Biology
  • Biotechnology
  • Bioinformatics

Background:

  • Osteoclasts are crucial multinucleated cells responsible for bone resorption.
  • Current methods for osteoclast identification in vitro assays are manual, labor-intensive, and lack objectivity.
  • Accurate quantification of osteoclasts is essential for understanding bone metabolism and diseases.

Purpose of the Study:

  • To develop a fully automated system, OC_Finder, for accurate and efficient identification of osteoclasts in microscopic images.
  • To overcome the limitations of manual osteoclast counting, including subjectivity and reproducibility issues.
  • To leverage deep learning for cell classification in biological imaging.

Main Methods:

  • Developed OC_Finder, an automated system integrating cell image segmentation using a watershed algorithm and cell classification via deep learning.
  • Trained and validated OC_Finder on microscopic images of osteoclasts differentiated from wild-type and Sh3bp2 precursor cells.
  • Assessed OC_Finder's performance against manual counting by human experts and on diverse datasets.

Main Results:

  • OC_Finder achieved 99.4% accuracy for segmentation and 98.1% accuracy for classification of osteoclasts.
  • The automated system demonstrated comparable accuracy to manual counting by experts.
  • Consistent performance was observed across datasets acquired with different microscopes and settings.

Conclusions:

  • OC_Finder provides a prompt, accurate, and unbiased method for osteoclast detection and classification.
  • Deep learning is a powerful tool for automating cell type identification in microscopic images, enhancing research efficiency.
  • The developed system can accelerate bone research by reducing manual workload and improving data reliability.