Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN

0575077 - ÚTIA 2024 RIV US eng C - Conference Paper (international conference)
Kerepecký, Tomáš - Liu, J. - Ng, X. W. - Piston, D. W. - Kamilov, U. S.
Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN.
Rhodes Island, Greece: IEEE, 2023. ISBN 978-1-7281-6328-4. In: Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE, 2023. ISBN 978-1-7281-6327-7.
[IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2023 /48./. Rhodes (GR), 04.06.2023-10.06.2023]
R&D Projects: GA ČR GA21-03921S
Institutional support: RVO:67985556
Keywords : Light-sheet fluorescence microscopy * Dual-view imaging * deep learning * image deconvolution
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://library.utia.cas.cz/separaty/2023/ZOI/kerepecky-0575077.pdf

Three-dimensional fluorescence microscopy often suffers from anisotropy, where the resolution along the axial direction is lower than that within the lateral imaging plane. We address this issue by presenting Dual-Cycle, a new framework for joint deconvolution and fusion of dual-view fluorescence images. Inspired by the recent Neuroclear method, Dual-Cycle is designed as a cycle-consistent generative network trained in a self-supervised fashion by combining a dual-view generator and prior-guided degradation model. We validate Dual-Cycle on both synthetic and real data showing its state-of-the-art performance without any external training data.
Permanent Link: https://hdl.handle.net/11104/0344936