Interactive Visualization Framework for Panoramic Super-Resolution Images Based on Localization Data

Combining super-resolution localization microscopy with panoramic digital pathology provides a powerful tool for biomedical researchers to observe the subcellar structure of the entire sample, but also brings the challenge of visualization of panoramic super-resolution image. However, current super-resolution image visualization methods are unable to process large-scale localization data, provide high-resolution panoramic images, and interactively visualize panoramic images. In response to the above problems, an interactive visualization framework for panoramic super-resolution images based on localization data is proposed, called PNanoViewer, which aims to achieve rapid interactive visualization of large-scale localization data on ordinary computers. This framework constructs the multi-resolution hierarchical structure of localization data based on the random sampling strategy, and visualizes multi-scale panoramic super-resolution image in an interactive way. Meanwhile, the block strategy and multi-thread parallel strategy are used to process large-scale localization data in batches to prevent memory overflow and speed up processing. Experimental results on tens to hundreds of millions of localization datasets show that the proposed PNanoViewer framework can visualize localization data with any size. Compared this framework with three currently popular super-resolution image visualization methods: PALMsiever, ThunderSTORM, and QC-STORM, PNanoViewer has obvious advantages in terms of data volume, resolution, and speed. At the same time, PNanoViewer also provides a useful exploration for the visualization of large-scale localization data.