3-D Image-to-Image Fusion in Lightsheet Microscopy by Two-Step Adversarial Network: Contribution to the Fusemycells Challenge
| Tipo de publicação: | Artigo |
| Citação: | |
| Publication status: | Published |
| Journal: | 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI) |
| Ano: | 2025 |
| Mês: | April |
| ISSN: | Electronic ISSN: 1945-8452 |
| URL: | https://ieeexplore.ieee.org/do... |
| DOI: | 10.1109/ISBI60581.2025.10981285 |
| Resumo: | 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. |
| Palavras-chave: | Deep Learning, Fuse-MyCells , Image-to-Image , Lightsheet Microscopy, Multiview Fusion |
| Autores | |
| Adicionado por: | [] |
| Total mark: | 0 |
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