Deep Learning-Assisted Co-registration of Full-Spectral Autofluorescence Lifetime Microscopic Images with H&E-Stained Histology Images

TL;DR

This paper introduces an unsupervised deep learning method for accurate co-registration of autofluorescence lifetime images with histology images, facilitating improved diagnosis and analysis of biological tissues.

Contribution

It presents a novel unsupervised image translation network that enhances co-registration accuracy between spectral autofluorescence lifetime images and histology images, applicable across various image formats.

Findings

Superiority of the method demonstrated in preliminary blind comparison.

Effective co-registration across different emission wavelengths and image formats.

Enables rapid visual identification of lung cancer and cellular characterization.

Abstract

Autofluorescence lifetime images reveal unique characteristics of endogenous fluorescence in biological samples. Comprehensive understanding and clinical diagnosis rely on co-registration with the gold standard, histology images, which is extremely challenging due to the difference of both images. Here, we show an unsupervised image-to-image translation network that significantly improves the success of the co-registration using a conventional optimisation-based regression network, applicable to autofluorescence lifetime images at different emission wavelengths. A preliminary blind comparison by experienced researchers shows the superiority of our method on co-registration. The results also indicate that the approach is applicable to various image formats, like fluorescence intensity images. With the registration, stitching outcomes illustrate the distinct differences of the spectral lifetime across an unstained tissue, enabling macro-level rapid visual identification of lung cancer and cellular-level characterisation of cell variants and common types. The approach could be effortlessly extended to lifetime images beyond this range and other staining technologies.