  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.
