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Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging

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Unsupervised Super Resolution Network for Hyperspectral Histologic Imaging.

Ling Ma^1,2, Armand Rathgeb¹, Minh Tran¹

¹Univ. of Texas at Dallas, Department of Bioengineering, Richardson, TX.

Proceedings of Spie--The International Society for Optical Engineering

|February 16, 2023

Summary

This summary is machine-generated.

Hyperspectral imaging (HSI) super-resolution reconstructs high-resolution images from low-resolution data using RGB guidance. This method reduces scanning time and storage for digital pathology applications.

Keywords:

Hyperspectral histologic imaging RGB guidance U-Net super resolution unsupervised

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Area of Science:

Digital pathology
Medical imaging
Computational pathology

Background:

Hyperspectral imaging (HSI) offers high sensitivity and specificity for cancer detection in histology.
Acquiring high-resolution HSI of whole slides is time-consuming and requires significant storage.
Reducing HSI data acquisition and storage is crucial for broader clinical adoption.

Purpose of the Study:

Develop an unsupervised super-resolution network for hyperspectral histologic imaging.
Utilize RGB digital histology images to guide the reconstruction of high-resolution HSI.
Improve HSI quality while reducing acquisition time and storage needs.

Main Methods:

Generated low-resolution HSI by down-sampling high-resolution HSI (10x magnification).
Registered high-resolution RGB images with corresponding HSI data.
Trained a modified U-Net neural network using unsupervised learning with low-resolution HSI and high-resolution RGB inputs.

Main Results:

Reconstructed high-resolution HSI exhibited similar spectral signatures to original data.
The super-resolution network improved image contrast in the generated HSI.
The method successfully reduced data acquisition time and storage requirements.

Conclusions:

Unsupervised super-resolution with RGB guidance effectively enhances HSI quality for digital pathology.
The proposed method addresses limitations of HSI acquisition, promoting its use in clinical settings.
This technique offers a viable solution for efficient hyperspectral data handling in medical imaging.