Globally optimal stitching of tiled 3D microscopic image acquisitions

By Stephan Preibisch, Stephan Saalfeld, Pavel Tomancak. Bioinform., 2009

2,311 citations recorded by the source corpus.

Motivation: Modern anatomical and developmental studies often require high-resolution imaging of large specimens in three dimensions (3D). Confocal microscopy produces high-resolution 3D images, but is limited by a relatively small field of view compared with the size of large biological specimens. Therefore, motorized stages that move the sample are used to create a tiled scan of the whole specimen. The physical coordinates provided by the microscope stage are not precise enough to allow direct reconstruction (Stitching) of the whole image from individual image stacks. Results: To optimally stitch a large collection of 3D confocal images, we developed a method that, based on the Fourier Shift Theorem, computes all possible translations between pairs of 3D images, yielding the best overlap in terms of the cross-correlation measure and subsequently finds the globally optimal configuration of the whole group of 3D images. This method avoids the propagation of errors by consecutive registration steps. Additionally, to compensate the brightness differences between tiles, we apply a smooth, non-linear intensity transition between the overlapping images. Our stitching approach is fast, works on 2D and 3D images, and for small image sets does not require prior knowledge about the tile configuration. Availability: The implementation of this method is available as an ImageJ plugin distributed as a part of the Fiji project (Fiji is just ImageJ: http://pacific.mpi-cbg.de/). Contact: tomancak@mpi-cbg.de

Subfields: Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/cbb742fb0dd3f8f9bcc5c2c14b723d93c9ff067d

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In Biophysics

In Computer Vision and Pattern Recognition

Citations in the corpus

The ImageJ ecosystem: an open platform for biomedical image analysis (2015, background): “The Stitching plugin (Preibisch et al., 2009) is a popular ImageJ option for combining such image collections into a single, cohesive output (see Fig.”
COMPREHENSIVE CLASSIFICATION OF RETINAL BIPOLAR NEURONS BY SINGLE-CELL TRANSCRIPTOMICS (2016, methodology): “The BC1B bipolar to unipolar transition was quantified from MitoP retinal sections stitched together using the pairwise stitching plugin of ImageJ (Preibisch et al., 2009).”
Multi-Domain Adversarial Learning (2019, methodology): “Image processing As the images were acquired using a 20X objective, they were stitched using ImageJ plugin (Preibisch et al., 2009) and down-scaled 2 times.”
ImageJ2: ImageJ for the next generation of scientific image data (2017, methodology): “References for plugins shown: TrackMate [67], MaMuT [150], Multiview Reconstruction [70, 71], MotherMachine Analyzer (MoMA) [151, 152], Sholl Analysis [74], Kymograph Builder [153], Z-Spacing Correction [154], Trainable Weka Segmentation [155], Pendent Drop [156], SciView [157], BigDataViewer [66], ”
BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples (2018, methodology): “It is based on identifying tile positions that minimize the distance between all links [14, 15].”
Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis. (2014, neutral): “…of numerous plane images still has issues such as unavoidable errors of mechanical stage accuracy, the need for additional calculations to correct for optical distortion and rotation for reconstitution, or the accumulation of huge data sets (Emmenlauer et al., 2009; Preibisch et al., 2009).”
Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front (2019, methodology): “After uploading, all spots of each TMA were stitched together into a single 10x7 spots file using the grid/collection stitching plugin (Preibisch et al., 2009) in ImageJ software (Fiji, version 2.0.0).”
A BaSiC tool for background and shading correction of optical microscopy images (2017, methodology): “BaSiC is available as a Fiji/ImageJ Plugin from the Fiji/ImageJ update site http://sites.imagej.net/BaSiC/ and from our software website https://www.helmholtz-muenchen.de/icb/research/groups/quantitative-single-cell-dynamics/software/basic/index.html, where we also provide ﬁve different microscopy d”
The Role of Macrophage Phenotype in Vascularization of Tissue Engineering Scaffolds (2014, neutral): “Briefly, the images were stitched together using the pairwise and grid/ collection stitching toolbox in FIJI [33] resulting in one image per sample.”