Computational protocol: Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens

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Protocol publication

[…] Raw open-top light-sheet microscopy images undergo several image processing steps to render the final images. The processing is written using a combination of MATLAB and ImageJ tools in Miji (a Java package that allows transfer of imaging data between MATLAB and ImageJ).Each individual image must be flat-field corrected for variations in intensity across the Gaussian light sheet, vignetting of the objective and tube lens collection optics, inter-pixel variations in the sensitivity of the sCMOS camera, and intrinsic aberrations in the open-top light-sheet microscope. To account for all of these effects, prior to imaging, a drop of a homogeneous fluorescence staining solution is imaged to acquire a reference image. Every subsequent raw image is normalized by this reference image to perform flat-field correction.The captured images correspond to oblique planes oriented at 45-deg with respect to the sample surface. However, when being stored digitally, these images form a data cube in which the images are oriented at 90 deg. Therefore, in post-processing, the images must be sheared by 45-deg in the x-z plane to create a trapezoidal data volume in which the tissue structure is undistorted. The sheared en-face images may exhibit minor striping artifacts due to mechanical vibrations. These artifacts are removed in ImageJ using a stripe filter based on a combined wavelet-Fourier filter [].After stripe-filtering, each image strip is registered to adjacent image strips using the ImageJ grid-stitching algorithm []. For digital registration of adjacent image strips, an overlap of 500 μm is used between the image strips, and a linear blending of each image strip is used to generate the final image mosaic. This operation is initially performed for a single en-face image mosaic in the x-y plane. The resulting registration and blending operations, determined from this single plane, are then applied to all of the imaging data at each depth, z. The imaging data now represents a 3D volume of multiple registered, blended, and stitched image mosaics. For fresh, scattering tissues, to further reduce the dimensionality of the data, and to extract only the irregular surface of the sample, an extended-depth of field (EDF) algorithm may be used []. For images acquired using two fluorophores, the cytoplasmic (eosin) and nuclear (DRAQ5) channels were false-colored to resemble H&E histology as in a recent study [], using the algorithm described by Giacomelli et al. []. […]

Pipeline specifications

Software tools ImageJ, Stitching
Applications SPIM, Microscopic phenotype analysis
Organisms Homo sapiens