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Autofluorescence lifetime images reveal unique characteristics of endogenousfluorescence in biological samples. Comprehensive understanding and clinicaldiagnosis rely on co-registration with the gold standard, histology images,which is extremely challenging due to the difference of both images. Here, weshow an unsupervised image-to-image translation network that significantlyimproves the success of the co-registration using a conventionaloptimisation-based regression network, applicable to autofluorescence lifetimeimages at different emission wavelengths. A preliminary blind comparison byexperienced researchers shows the superiority of our method on co-registration.The results also indicate that the approach is applicable to various imageformats, like fluorescence intensity images. With the registration, stitchingoutcomes illustrate the distinct differences of the spectral lifetime across anunstained tissue, enabling macro-level rapid visual identification of lungcancer and cellular-level characterisation of cell variants and common types.The approach could be effortlessly extended to lifetime images beyond thisrange and other staining technologies.

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