Computational framework for generating large panoramic super-resolution images from localization microscopy

Yue Du; Chenze Wang; Chen Zhang; Lingyun Guo; Yanzhu Chen; Meng Yan; Qianghui Feng; Mingtao Shang; Weibing Kuang; Zhengxia Wang; Zhen-Li Huang

doi:10.1364/BOE.433489

Computational framework for generating large panoramic super-resolution images from localization microscopy

Biomed Opt Express. 2021 Jul 12;12(8):4759-4778. doi: 10.1364/BOE.433489. eCollection 2021 Aug 1.

Authors

Yue Du¹, Chenze Wang², Chen Zhang¹, Lingyun Guo², Yanzhu Chen², Meng Yan², Qianghui Feng², Mingtao Shang¹, Weibing Kuang¹, Zhengxia Wang^{2

3}, Zhen-Li Huang^{4

5}

Affiliations

¹ Britton Chance Center and MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China.
² School of Computer Science and Technology, Hainan University, Haikou 570228, China.
³ zxiawang@hainanu.edu.cn.
⁴ School of Biomedical Engineering, Hainan University, Haikou 570228, China.
⁵ Huang2020@hainanu.edu.cn.

Abstract

Combining super-resolution localization microscopy with pathology creates new opportunities for biomedical researches. This combination requires a suitable image mosaic method for generating a panoramic image from many overlapping super-resolution images. However, current image mosaic methods are not suitable for this purpose. Here we proposed a computational framework and developed an image mosaic method called NanoStitcher. We generated ground truth datasets and defined criteria to evaluate this computational framework. We used both simulated and experimental datasets to prove that NanoStitcher exhibits better performance than two representative image mosaic methods. This study is helpful for the mature of super-resolution digital pathology.