Globally optimal stitching of tiled 3D microscopic image acquisitions
ABSTRACT 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.
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.
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/).
Full-textDOI: · Available from: Stephan Preibisch, Jul 28, 2015
- SourceAvailable from: Farshid Guilak
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ABSTRACT: Tissue and biomaterial microenvironments provide architectural cues that direct important cell behaviors including cell shape, alignment, migration, and resulting tissue formation. These architectural features may be presented to cells across multiple length scales, from nanometers to millimeters in size. In this study, we examined how architectural cues at two distinctly different length scales, "micro-scale" cues on the order of ∼1-2 μm, and "meso-scale" cues several orders of magnitude larger (>100 μm), interact to direct aligned neo-tissue formation. Utilizing a micro-photopatterning (μPP) model system to precisely arrange cell-adhesive patterns, we examined the effects of substrate architecture at these length scales on human mesenchymal stem cell (hMSC) organization, gene expression, and fibrillar collagen deposition. Both micro- and meso-scale architectures directed cell alignment and resulting tissue organization, and when combined, meso cues could enhance or compete against micro-scale cues. As meso boundary aspect ratios were increased, meso-scale cues overrode micro-scale cues and controlled tissue alignment, with a characteristic critical width (∼500 μm) similar to boundary dimensions that exist in vivo in highly aligned tissues. Meso-scale cues acted via both lateral confinement (in a cell-density-dependent manner) and by permitting end-to-end cell arrangements that yielded greater fibrillar collagen deposition. Despite large differences in fibrillar collagen content and organization between μPP architectural conditions, these changes did not correspond with changes in gene expression of key matrix or tendon-related genes. These findings highlight the complex interplay between geometric cues at multiple length scales and may have implications for tissue engineering strategies, where scaffold designs that incorporate cues at multiple length scales could improve neo-tissue organization and resulting functional outcomes.Biomaterials 09/2014; 35(38). DOI:10.1016/j.biomaterials.2014.08.047 · 8.31 Impact Factor
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- "To correct for movement of regions of interest due to root growth, images were registered using the MultiStackReg v1.4 plug-in (Thevenaz et al., 1998, modified by Brad Busse). When needed, multipoint time series were stitched together using the grid/collection stitching plug-in (Preibisch et al., 2009) using a maximum intensity fusion method. Mean intensity values for regions of interest were calculated as follows: Background was subtracted from all measured intensities; donor (DxDm) and acceptor (DxAm) intensities under donor excitation were corrected against acceptor intensity under acceptor excitation (AxAm) to correct for intensity fluctuation caused by focus drift, root movement, or changing sensor protein levels (during long-term measurements). "
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