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CellBinDB: a large-scale multimodal annotated dataset for cell segmentation with benchmarking of universal models.

Can Shi1,2, Jinghong Fan1,2, Zhonghan Deng1

  • 1BGI Research, Shenzhen 518083, China.

Gigascience
|June 24, 2025
PubMed
Summary

A new dataset, CellBinDB, aids in developing universal cell segmentation models for biological image analysis. Benchmarking reveals complex cell shapes hinder accuracy, while image gradients improve boundary detection.

Keywords:
benchmarkcell segmentationdatasetuniversal models

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Area of Science:

  • Computational Biology
  • Bioimage Analysis
  • Digital Pathology

Background:

  • Cell segmentation is vital for quantitative biological image analysis.
  • Deep learning models excel but lack generalizability due to modality-specific tuning.
  • A need exists for comprehensive datasets to train universal models and evaluate segmentation techniques.

Purpose of the Study:

  • Introduce CellBinDB, a large-scale multimodal annotated dataset.
  • Facilitate training of universal cell segmentation models.
  • Benchmark current cell segmentation technologies.

Main Methods:

  • Created CellBinDB with over 1,000 annotated images from human and mouse tissues.
  • Included diverse staining: DAPI, ssDNA, H&E, and multiplex immunofluorescence.
  • Benchmarked 8 state-of-the-art cell segmentation methods using the dataset.

Main Results:

  • CellBinDB covers >30 normal and diseased tissue types.
  • Complex cell shapes were found to reduce segmentation accuracy.
  • Higher image gradients were correlated with improved boundary detection.

Conclusions:

  • CellBinDB supports development of versatile cell segmentation solutions.
  • Insights gained can refine segmentation strategies for diverse imaging scenarios.
  • The dataset aids in advancing quantitative biological image analysis.