• Advanced Photonics
  • Vol. 6, Issue 6, (2024)
Hassan Dana, Domínguez Jesús, Midtvedt Benjamin, Klein Moberg Henrik, Pineda Jesús, Langhammer Christoph, Volpe Giovanni, Homs Corbera Antoni, Beck Adiels Caroline
Author Affiliations
  • CherryBiotech
  • Elvesys
  • University of Gothenburg
  • Chalmers University of Technology
  • show 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.
    Manuscript Accepted: Aug. 19, 2024
    Posted: Oct. 28, 2024
    DOI: AP