Open AccessComputational framework for generating large panoramic super-resolution images from localization microscopy
Author(s) -
Yue Du,
Chenze Wang,
Chen Zhang,
Lingyun Guo,
Yanzhu Chen,
Yan Meng,
Qianghui Feng,
Mingtao Shang,
Weibing Kuang,
Zhengxia Wang,
Zhen-Li Huang
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.433489
Subject(s) - mosaic , computer science , computer vision , artificial intelligence , image resolution , image (mathematics) , resolution (logic) , image processing , digital image , ground truth , digital image processing , computational model , digital pathology , pattern recognition (psychology) , archaeology , history
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.