Translating Light-Sheet Microscopy Images to Virtual H&E Using CycleGAN
This enables pathologists to visualize fluorescence data in a familiar format and integrate it with standard H&E-based analysis workflows, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of converting fluorescence microscopy images to H&E-like histopathology images using a CycleGAN approach, resulting in realistic pseudo H&E images that preserve morphological structures and adopt H&E-like color characteristics.
Histopathology analysis relies on Hematoxylin and Eosin (H&E) staining, but fluorescence microscopy offers complementary information. Converting fluorescence images to H&E-like appearance can aid interpretation and integration with standard workflows. We present a Cycle-Consistent Adversarial Network (CycleGAN) approach for unpaired image-to-image translation from multi-channel fluorescence microscopy to pseudo H&E stained histopathology images. The method combines C01 and C02 fluorescence channels into RGB and learns a bidirectional mapping between fluorescence and H&E domains without paired training data. The architecture uses ResNet-based generators with residual blocks and PatchGAN discriminators, trained with adversarial, cycle-consistency, and identity losses. Experiments on fluorescence microscopy datasets show the model generates realistic pseudo H&E images that preserve morphological structures while adopting H&E-like color characteristics. This enables visualization of fluorescence data in a format familiar to pathologists and supports integration with existing H&E-based analysis pipelines.