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 reduced 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 phototoxicity. 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 volumes 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 downsampled 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 challenges related to high data resolution, the necessity of global context, and the preservation of high-frequency details. Experimental 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.

翻译：光片显微镜是一种强大的三维成像技术，它解决了传统光学和共聚焦显微镜的局限性，但存在穿透深度低以及在更大深度下图像质量下降的问题。多视角光片显微镜通过融合多个视角提高了三维分辨率，但同时也增加了复杂性和光子预算，可能导致光漂白和光毒性。与IEEE ISBI 2025会议联合组织的FuseMyCells挑战，旨在对基于深度学习的、从单一三维视图融合高质量三维体积的解决方案进行基准测试，这有望简化操作流程并节约光子预算。在本工作中，我们提出了一种基于两步流程的、对FuseMyCells挑战的贡献方案。第一步处理图像的下采样版本以捕获整个感兴趣区域，而第二步采用基于图像块的方法进行高分辨率推断，并引入对抗损失以提升视觉结果。该方法解决了与高数据分辨率、全局上下文必要性以及高频细节保留相关的挑战。实验结果证明了我们方法的有效性，突显了其在提升三维图像融合质量和扩展光片显微镜能力方面的潜力。细胞核与细胞膜的平均结构相似性指数分别大于0.85和0.91。