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@ARTICLE{,
    author = {Wodzinski, Marek and M{\"{u}}ller, Henning},
  keywords = {Deep Learning, Fuse-MyCells , Image-to-Image , Lightsheet Microscopy, Multiview Fusion },
     month = apr,
     title = {3-D Image-to-Image Fusion in Lightsheet Microscopy by Two-Step Adversarial Network: Contribution to the Fusemycells Challenge},
   journal = {2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)},
      year = {2025},
      issn = {Electronic ISSN: 1945-8452},
       url = {https://ieeexplore.ieee.org/document/10981285/keywords#keywords},
       doi = {10.1109/ISBI60581.2025.10981285},
  abstract = {Lightsheet microscopy is a powerful 3-D imaging technique that addresses limitations of traditional optical and confocal microscopy but suffers from a low penetration depth and re-duced image quality at greater depths. Multiview lightsheet microscopy improves 3-D resolution by combining multiple views but simultaneously increasing the complexity and the photon budget, leading to potential photobleaching and pho-totoxicity. The FuseMyCells challenge, organized in conjunction with the IEEE ISBI 2025 conference, aims to benchmark deep learning-based solutions for fusing high-quality 3-D vol-umes from single 3-D views, potentially simplifying procedures and conserving the photon budget. In this work, we propose a contribution to the FuseMyCells challenge based on a two-step procedure. The first step processes a down-sampled version of the image to capture the entire region of interest, while the second step uses a patch-based approach for high-resolution inference, incorporating adversarial loss to enhance visual outcomes. This method addresses chal-lenges related to high data resolution, the necessity of global context, and the preservation of high-frequency details. Ex-perimental results demonstrate the effectiveness of our approach, highlighting its potential to improve 3-D image fusion quality and extend the capabilities of lightsheet microscopy. The average SSIM for the nucleus and membranes is greater than 0.85 and 0.91, respectively.}
}