NRStitcher: Non-rigid stitching of terapixel-scale volumetric images.

Miettinen, Arttu; Vogiatzis Oikonomidis, Ioannis; Bonnin, Anne; Stampanoni, Marco (2019). NRStitcher: Non-rigid stitching of terapixel-scale volumetric images. Bioinformatics, 35(24), pp. 5290-5297. Oxford University Press 10.1093/bioinformatics/btz423

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SUMMARY In modern microscopy the field of view is often increased by obtaining an image mosaic, where multiple sub-images are taken side by side and combined post-acquisition. Mosaic imaging often leads to long imaging times that can increase the probability of sample deformation during the acquisition due to, e.g., changes in the environment, damage caused by the radiation used to probe the sample, or biologically induced deterioration. Here we propose a technique, based on local phase correlation, to detect the deformations and construct an artifact-free image mosaic from deformed sub-images. The implementation of the method supports distributed computing and can be used to generate teravoxel-size mosaics. We demonstrate its capabilities by assembling a 5.6 teravoxel tomographic image mosaic of microvasculature in whole mouse brain. The method is compared to existing rigid stitching implementations designed for very large datasets, and observed to create artifact-free image mosaics in comparable runtime with the same hardware resources. AVAILABILITY AND IMPLEMENTATION The stitching software and C ++/Python source code are available at GitHub (https://github.com/arttumiettinen/pi2) along with an example dataset and user instructions.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Anatomy > Anatomy
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Anatomy > Topographical and Clinical Anatomy

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Vogiatzis Oikonomidis, Ioannis

Subjects:

500 Science > 570 Life sciences; biology

ISSN:

1367-4803

Publisher:

Oxford University Press

Language:

English

Submitter:

Johannes Schittny

Date Deposited:

23 Jan 2020 14:50

Last Modified:

23 Jan 2020 14:50

Publisher DOI:

10.1093/bioinformatics/btz423

PubMed ID:

31116382

BORIS DOI:

10.7892/boris.137851

URI:

https://boris.unibe.ch/id/eprint/137851

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