Wang Shu, Liu Xiaoxiang, Li Yueying, Sun Xinquan, Li Qi, She Yinhua, Xu Yixuan, Huang Xingxin, Lin Ruolan, Kang Deyong, Wang Xingfu, Tu Haohua, Liu Wenxi, Huang Feng, Chen Jianxin
College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China.
College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China.
Nat Commun. 2023 Sep 5;14(1):5393. doi: 10.1038/s41467-023-41165-1.
Stitched fluorescence microscope images inevitably exist in various types of stripes or artifacts caused by uncertain factors such as optical devices or specimens, which severely affects the image quality and downstream quantitative analysis. Here, we present a deep learning-based Stripe Self-Correction method, so-called SSCOR. Specifically, we propose a proximity sampling scheme and adversarial reciprocal self-training paradigm that enable SSCOR to utilize stripe-free patches sampled from the stitched microscope image itself to correct their adjacent stripe patches. Comparing to off-the-shelf approaches, SSCOR can not only adaptively correct non-uniform, oblique, and grid stripes, but also remove scanning, bubble, and out-of-focus artifacts, achieving the state-of-the-art performance across different imaging conditions and modalities. Moreover, SSCOR does not require any physical parameter estimation, patch-wise manual annotation, or raw stitched information in the correction process. This provides an intelligent prior-free image restoration solution for microscopists or even microscope companies, thus ensuring more precise biomedical applications for researchers.
拼接后的荧光显微镜图像不可避免地存在各种由光学设备或标本等不确定因素引起的条纹或伪影，这严重影响了图像质量和下游的定量分析。在此，我们提出了一种基于深度学习的条纹自校正方法，即SSCOR。具体而言，我们提出了一种邻近采样方案和对抗性互训练范式，使SSCOR能够利用从拼接后的显微镜图像本身采样的无条纹补丁来校正其相邻的条纹补丁。与现有方法相比，SSCOR不仅可以自适应地校正不均匀、倾斜和网格条纹，还可以去除扫描、气泡和失焦伪影，在不同的成像条件和模式下均实现了最先进的性能。此外，SSCOR在校正过程中不需要任何物理参数估计、逐补丁手动标注或原始拼接信息。这为显微镜工作者甚至显微镜公司提供了一种无需智能先验的图像恢复解决方案，从而为研究人员确保了更精确的生物医学应用。
