美国索尔克生物研究所Uri Manor团队通过深度学习提升点扫描超分辨率成像。相关论文于2021年3月8日在线发表在《自然—方法学》杂志上。

据研究人员介绍，点扫描成像系统受益于任意定义的像素大小，这是高分辨率细胞和组织成像中最广泛使用的工具之一。点扫描系统的分辨率、速度、样品保存和信噪比（SNR）难以同时优化。

研究人员发现，这些限制可以通过使用基于深度学习的在点扫描系统上获取的欠采样图像的超采样来解决，研究人员将其称为点扫描超分辨率（PSSR）成像。研究人员设计了一个“crappifier”，通过计算降低高SNR、高像素分辨率的真实图像来模拟低SNR、低分辨率的对等物，来训练PSSR模型，从而可以还原现实世界中欠采样的图像。

对于高时空分辨率的荧光延时数据，研究人员开发了一种“多帧” PSSR方法，该方法使用相邻帧中的信息来改善模型预测。PSSR有助于以其他方式无法实现的分辨率、速度和灵敏度进行点扫描图像采集。PSSR的所有培训数据、模型和代码均可在3DEM.org上公开获得。 

附：英文原文

Title: Deep learning-based point-scanning super-resolution imaging

Author: Linjing Fang, Fred Monroe, Sammy Weiser Novak, Lyndsey Kirk, Cara R. Schiavon, Seungyoon B. Yu, Tong Zhang, Melissa Wu, Kyle Kastner, Alaa Abdel Latif, Zijun Lin, Andrew Shaw, Yoshiyuki Kubota, John Mendenhall, Zhao Zhang, Gulcin Pekkurnaz, Kristen Harris, Jeremy Howard, Uri Manor

Issue&Volume: 2021-03-08

Abstract: Point-scanning imaging systems are among the most widely used tools for high-resolution cellular and tissue imaging, benefiting from arbitrarily defined pixel sizes. The resolution, speed, sample preservation and signal-to-noise ratio (SNR) of point-scanning systems are difficult to optimize simultaneously. We show these limitations can be mitigated via the use of deep learning-based supersampling of undersampled images acquired on a point-scanning system, which we term point-scanning super-resolution (PSSR) imaging. We designed a ‘crappifier’ that computationally degrades high SNR, high-pixel resolution ground truth images to simulate low SNR, low-resolution counterparts for training PSSR models that can restore real-world undersampled images. For high spatiotemporal resolution fluorescence time-lapse data, we developed a ‘multi-frame’ PSSR approach that uses information in adjacent frames to improve model predictions. PSSR facilitates point-scanning image acquisition with otherwise unattainable resolution, speed and sensitivity. All the training data, models and code for PSSR are publicly available at 3DEM.org.

DOI: 10.1038/s41592-021-01080-z

Source: https://www.nature.com/articles/s41592-021-01080-z