Transforming hyperspectral data into insight: the DREAM approach for pathology

Area of Science:

• Biomedical Optics
• Quantitative Pathology
• Microscopy

Background:

• Quantitative pathology relies on staining and subjective interpretation, limiting its objectivity.
• Autofluorescence imaging provides a label-free alternative but faces challenges in visualizing high-dimensional spectral data.
• Reproducible analysis of complex autofluorescence datasets is crucial for clinical translation.

Purpose of the Study:

• To develop a method for condensing high-dimensional autofluorescence spectra into an interpretable format.
• To enable label-free, high-contrast visualization of histological structures in unstained tissues.
• To improve the objectivity and reproducibility of quantitative pathology using enhanced spectral imaging.

Main Methods:

• Dimensionality Reduction for Enhanced Autofluorescence Microscopy (DREAM) utilizes phasor-based tools.
• Condensing multi-excitation emission spectra into a compact, information-rich representation.
• Application to unstained human esophageal tissue samples for spectral analysis.

Main Results:

• DREAM successfully generated high-contrast visualizations distinguishing key histological structures without staining.
• Quantitative assessments demonstrated improvements in colorfulness, sharpness, and consistency compared to single-laser methods.
• The method effectively processed complex autofluorescence datasets for enhanced spectral visualization.

Conclusions:

• DREAM provides an effective strategy for label-free quantitative pathology through enhanced spectral visualization.
• The method addresses limitations in current autofluorescence imaging, enabling objective analysis.
• DREAM holds significant potential for advancing reproducible, label-free diagnostic tools in histopathology.