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NOISe: Nuclei-Aware Osteoclast Instance Segmentation for Mouse-to-Human Domain Transfer.

Sai Kumar Reddy Manne1, Brendan Martin1, Tyler Roy2

  • 1Northeastern University.

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops
|December 11, 2024
PubMed
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This study introduces a novel deep learning method for automated osteoclast cell image segmentation, achieving high accuracy for both mouse and human cells. The publicly released dataset and code accelerate osteoporosis research by enabling reproducible analysis.

Area of Science:

  • Biomedical Image Analysis
  • Computational Biology
  • Osteoporosis Research

Background:

  • Osteoclast cell image analysis is crucial for osteoporosis research but relies on laborious manual annotation.
  • Existing automated methods lack full instance segmentation capabilities and do not provide accessible code or datasets.

Purpose of the Study:

  • To develop a fully automated deep learning method for osteoclast instance segmentation.
  • To create and release a large-scale annotated dataset for osteoclast images.
  • To enable reproducible and accelerated research in osteoporosis.

Main Methods:

  • Developed a deep learning instance segmentation model applicable to both mouse and human osteoclast cells.
  • Created a novel nuclei-aware osteoclast instance segmentation (NOISe) training strategy.

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Last Updated: Jun 5, 2025

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  • Compiled a dataset of approximately 2 × 10^5 expert-annotated mouse osteoclast masks.

    • Main Results:

    • Achieved 0.82 mAP$_{0.5}$ cross-validation performance for mouse osteoclasts.
    • Improved human osteoclast segmentation performance from 0.60 to 0.82 mAP$_{0.5}$ using the NOISe strategy.
    • Successfully automated the full osteoclast instance segmentation task.

    Conclusions:

    • The presented deep learning method and dataset significantly advance automated osteoclast image analysis.
    • The NOISe strategy enhances model generalizability and accuracy for osteoclast segmentation.
    • Publicly releasing the code, models, and dataset promotes reproducibility and accelerates osteoporosis research.