Chen Yanzhu, Xu Zhiwang, Ren Shijie, Huang Zhen-Li, Wang Zhengxia
School of Computer Science and Technology, Hainan University, Haikou 570228, China.
School of Biomedical Engineering, Hainan University, Sanya 572025, China.
Biomed Opt Express. 2024 Aug 21;15(9):5411-5428. doi: 10.1364/BOE.534658. eCollection 2024 Sep 1.
Super-resolution panoramic pathological imaging provides a powerful tool for biologists to observe the ultrastructure of samples. Localization data can maintain the essential ultrastructural information of biological samples with a small storage space, and also provides a new opportunity for stitching super-resolution images. However, the existing image stitching methods based on localization data cannot accurately calculate the registration offset of sample regions with no or few structural points and thus lead to registration errors. Here, we proposed a stitching framework called PNanoStitcher. The framework fully utilizes the distribution characteristics of the background fluorescence noise in the stitching region and solves the stitching failure in sample regions with no or few structural points. We verified our method using both simulated and experimental datasets, and compared it with existing stitching methods. PNanoStitcher achieved superior stitching results on biological samples with no structural and few structural regions. The study provides an important driving force for the development of super-resolution digital pathology.
超分辨率全景病理成像为生物学家观察样本的超微结构提供了一个强大的工具。定位数据能够以较小的存储空间保留生物样本的基本超微结构信息，同时也为超分辨率图像拼接提供了新契机。然而，现有的基于定位数据的图像拼接方法无法准确计算没有或只有很少结构点的样本区域的配准偏移量，从而导致配准错误。在此，我们提出了一种名为PNanoStitcher的拼接框架。该框架充分利用了拼接区域背景荧光噪声的分布特征，解决了没有或只有很少结构点的样本区域的拼接失败问题。我们使用模拟数据集和实验数据集对我们的方法进行了验证，并与现有的拼接方法进行了比较。PNanoStitcher在没有结构区域和只有很少结构区域的生物样本上取得了优异的拼接结果。该研究为超分辨率数字病理学的发展提供了重要推动力。
