基于定位数据的全景超分辨图像交互可视化框架

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

超分辨定位成像和全景数字病理结合,为研究人员观察整个样本的亚细胞结构提供了有力工具,同时也带来了全景超分辨图像海量数据可视化的挑战.然而,现有的超分辨图像可视化方法无法处理大规模定位数据、不能提供高分辨全景图像和无法交互可视化全景图像.针对以上问题,提出了一个基于定位数据的全景超分辨图像交互可视化框架,称为PNanoViewer,旨在普通计算机上实现大规模定位数据的快速交互可视化.该框架基于随机采样策略构建定位数据的多分辨率层级结构,以交互方式可视化多尺度全景超分辨图像;同时采用分块策略和多线程并行策略,分批次处理大规模定位数据,既防止内存溢出又加快了处理速度.从数千万到数亿个定位点数据集上的实验结果表明,PNanoViewer框架能够可视化任意规模的定位数据.将该框架与目前3种流行的超分辨图像可视化方法PALMsiever,ThunderSTORM,QCSTORM从数据量、分辨率和速度3个方面进行对比,PNanoViewer都具有明显优势.同时也为大规模定位数据的可视化提供了一个有益的探索.

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 superresolution 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.