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  3. 2d Multimodal Image Collection For Fluorescence Prediction From Transmitted Light Microscopy.
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  3. 2d Multimodal Image Collection For Fluorescence Prediction From Transmitted Light Microscopy.

Related Experiment Video

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
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Published on: September 23, 2013

2D Multimodal Image Collection for Fluorescence Prediction from Transmitted Light Microscopy.

Dorian Kauffmann1,2, Guillaume Gay1,2, Julio Mateos-Langerak3,4

  • 1Laboratoire d'informatique, de robotique et de microélectronique de Montpellier, LIRMM, University of Montpellier CNRS, Montpellier, France.

Scientific Data
|March 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

The Light My Cells Database offers 56,984 microscopy images for training machine learning models to predict fluorescence from label-free images. This open-access dataset aids in developing generalizable deep learning models for cell analysis.

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Area of Science:

  • Bioimage analysis
  • Machine learning in biology
  • Cellular imaging

Background:

  • Accurate fluorescence prediction from label-free microscopy is crucial for advancing biological research.
  • Existing datasets often lack the diversity and scale needed for robust machine learning model training.

Purpose of the Study:

  • To introduce the Light My Cells Database, a comprehensive open-access resource for machine learning in fluorescence prediction.
  • To provide a diverse collection of microscopy images to support the development of generalizable deep learning models.

Main Methods:

  • Compiled 2,574 acquisition sets and 56,984 2D microscopy images from 30 studies across 8 imaging centers.
  • Paired transmitted light images (bright-field, phase contrast, DIC) with fluorescence images of subcellular structures (nucleus, mitochondria, tubulin, actin).
  • Standardized images in OME-TIFF format with REMBI-compliant metadata and applied a preprocessing pipeline for harmonization.

    • Main Results:

    • The database contains a wide diversity of biological samples, imaging modalities, and acquisition systems, reflecting real-world microscopy variability.
    • Standardized images and rich metadata facilitate downstream applications like in silico labeling, segmentation, and cell profiling.
    • The dataset is suitable for training and benchmarking generalizable deep learning models for label-free imaging analysis.

    Conclusions:

    • The Light My Cells Database is a valuable resource for advancing machine learning applications in microscopy and bioimage analysis.
    • This open-access dataset will accelerate the development of AI-powered tools for label-free cellular imaging and analysis.
    • Facilitates in silico labeling and cell profiling, enabling new avenues in biological discovery.