Scanning microscopy systems, such as confocal and multiphoton microscopy, are powerful imaging tools for probing deep into biological tissue. However, scanning systems have an inherent trade-off between acquisition time, field of view, phototoxicity, and image quality, often resulting in noisy measurements when fast, large field of view, and/or gentle imaging is needed. Deep learning could be used to denoise noisy microscopy measurements, but these algorithms can be prone to hallucination, which can be disastrous for medical and scientific applications. We propose a method to simultaneously denoise and predict pixel-wise uncertainty for scanning microscopy systems, improving algorithm trustworthiness and providing statistical guarantees for deep learning predictions. Furthermore, we propose to leverage this learned, pixel-wise uncertainty to drive an adaptive acquisition technique that rescans only the most uncertain regions of a sample, saving time and reducing the total light dose to the sample. We demonstrate our method on experimental confocal and multiphoton microscopy systems, showing that our uncertainty maps can pinpoint hallucinations in the deep learned predictions. Finally, with our adaptive acquisition technique, we demonstrate up to 16X reduction in acquisition time and total light dose while successfully recovering fine features in the sample and reducing hallucinations. We are the first to demonstrate distribution-free uncertainty quantification for a denoising task with real experimental data and the first to propose adaptive acquisition based on reconstruction uncertainty.

翻译：扫描显微成像系统，如共聚焦和多光子显微镜，是深入探测生物组织的有力成像工具。然而，扫描系统在采集时间、视场、光毒性和图像质量之间存在固有的权衡，通常在需要快速、大视场和/或温和成像时导致测量结果噪声较大。深度学习可用于去噪显微测量中的噪声，但这些算法容易产生幻觉伪影，这对于医学和科学应用可能是灾难性的。我们提出了一种方法，可同时为扫描显微系统进行去噪和像素级不确定性预测，从而提高算法的可信度，并为深度学习预测提供统计保证。此外，我们提出利用这种学习到的像素级不确定性来驱动自适应采集技术，该技术仅对样本中最不确定的区域进行重新扫描，从而节省时间并减少样本的总光照剂量。我们在实验性共聚焦和多光子显微镜系统上验证了我们的方法，表明我们的不确定性图能够精确定位深度学习预测中的幻觉伪影。最后，通过我们的自适应采集技术，我们实现了高达16倍的采集时间和总光照剂量的减少，同时成功恢复了样本的精细特征并减少了幻觉伪影。我们是首个在真实实验数据上为去噪任务展示无分布不确定性量化的研究，也是首个提出基于重建不确定性的自适应采集方法的研究。