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Area of Science:

  • Biomedical Imaging
  • Computational Biology
  • Histopathology

Background:

  • Label-free autofluorescence lifetime imaging microscopy (FLIM) offers rich biological information.
  • Current FLIM analysis lacks rapid, precise interpretation without histology references.
  • Co-registration of FLIM and histology images is complex and often unavailable.

Purpose of the Study:

  • To develop a deep learning (DL) approach for generating virtual Hematoxylin and Eosin (H&E) staining from FLIM images.
  • To enable accurate, real-time cellular-level interpretation of unstained biological samples.
  • To facilitate biomarker-free tissue histology using FLIM.

Main Methods:

  • Utilized a deep learning model combined with an image quality metric.
  • Generated virtual H&E images from label-free FLIM data.
  • Compared reconstructions using lifetime information versus intensity-only images.

Main Results:

  • Achieved clinical-grade virtual H&E staining from label-free FLIM images.
  • Demonstrated that lifetime information enhances virtual staining accuracy compared to intensity-only.
  • Successfully identified distinct lifetime signatures for seven common tumor microenvironment cell types.

Conclusions:

  • The DL-based virtual H&E staining method provides instant and accurate interpretation of FLIM images.
  • This approach overcomes the need for co-registered histology, simplifying analysis.
  • Opens new avenues for biomarker-free tissue histology across various cancer types using FLIM.