2021
DOI: 10.1364/boe.433489
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Computational framework for generating large panoramic super-resolution images from localization microscopy

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 computationa… Show more

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Cited by 11 publications
(10 citation statements)
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“…We note that, at present, the stitched views are based on coarse alignments between matching gold fiduciaries in the overlapping regions. Optimal stitching with sub-diffractive registration precisions (on par with the image resolutions) requires that the optical aberrations are properly corrected first , and is currently under development. Imaging of the same 1 mm 2 FOVs would have taken 1–3 days if using DNA-PAINT with a standard 50 μm × 50 μm FOV or 6–8 h even with ASTER .…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…We note that, at present, the stitched views are based on coarse alignments between matching gold fiduciaries in the overlapping regions. Optimal stitching with sub-diffractive registration precisions (on par with the image resolutions) requires that the optical aberrations are properly corrected first , and is currently under development. Imaging of the same 1 mm 2 FOVs would have taken 1–3 days if using DNA-PAINT with a standard 50 μm × 50 μm FOV or 6–8 h even with ASTER .…”
Section: Resultsmentioning
confidence: 99%
“…The latter two schemes also create homogeneous illumination across the FOV for more uniform imaging than the Gaussian beam used in this work. Image quality can also be improved by correcting for optical aberrations, which, even with optimized optics and light paths, cannot be neglected given the large FOVs. ,, Such aberrations also pose challenges when stitching images from multiple FOVs . Aside from these potential improvements, PRIME-PAINT is by design amenable to automation through integration of programmable fluidics, FOV stitching and buffer exchange, and online data processing. , Leveraging recent progress in multiplexing strategies and 3D calibration over extended depth and large FOVs, , 3D PRIME-PAINT imaging of many more targets over several mm 2 sample areas should be readily feasible.…”
Section: Conclusion and Discussionmentioning
confidence: 99%
“…Therefore, this technology has been extensively used in biomedical research and other fields. Now, biomedical researchers hope to obtain more sample information in a shorter time [14][15][16][17][18][19]. It is necessary to observe the sample microstructure clearly and collect its macroscopic information quickly.…”
Section: Introductionmentioning
confidence: 99%
“…The trend has been pointed to developing high-throughput super-resolution imaging techniques for high content screening [3][4][5] . The typical strategy is for automated microscope to acquire small field of view (FOV) images one by one 6 and generate a mosaic image by post processing 7 . However, hardware automation is often not available in the typical microscopes, and some biological samples are not suitable to be volumetrically scanned.…”
Section: Introductionmentioning
confidence: 99%
“…However, hardware automation is often not available in the typical microscopes, and some biological samples are not suitable to be volumetrically scanned. Moreover, it is not easy to stitch the super resolution images with accuracy comparable to its high spatial resolution 7 . As most biological samples contain rich structural information in three dimensions, it becomes particularly challenging to obtain the whole-cell-scale 3D single-molecule-resolution image at high throughput.…”
Section: Introductionmentioning
confidence: 99%