Deep learning-assisted co-registration of full-spectral autofluorescence lifetime microscopic images with H&E-stained histology images.

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 in-tensity imag

SUBMITTER: Wang Q 

PROVIDER: S-EPMC9586936 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature