Hassan Dana, Domínguez Jesús, Midtvedt Benjamin, Klein Moberg Henrik, Pineda Jesús, Langhammer Christoph, Volpe Giovanni, Homs Corbera Antoni, Beck Adiels Caroline
Author Affiliations
CherryBiotechElvesysUniversity of GothenburgChalmers University of Technologyshow less
Abstract
Recent advancements in deep learning have propelled the virtual transformation of microscopy images across optical modalities, enabling unprecedented multi-modal imaging analysis hitherto impossible. Despite these strides, the integration of such algorithms into scientists’ daily routines and clinical trials remains limited, largely due to a lack of recognition within the irrespective fields and the plethora of available transformation methods. To address this, we present a structured overview of cross-modality transformations, encompassing applications, datasets and implementations, aimed at unifying this evolving field. Our review focuses on deep learning solutions for two key applications: contrast enhancement of targeted features within images and resolution enhancements. We identify cross-modality transformations as a valuable asset for biologists. Notably, they enable high-contrast, high-specificity imaging akin to fluorescence microscopy without the need for laborious, costly, and disruptive physical staining pro- cedures. Additionally, they facilitate the realisation of imaging with properties that would typically require costly or complex physical modifications, such as achieving super-resolution capabilities. By consolidating the current state of research in this review, we aim to catalyse further investigation and development, ultimately bringing the potential of cross-modality transformations into the hands of researchers and clinicians alike.