PubMed · 13664940
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
J R MEYER-ARENDT. 1959. Internal structures as artifacts.. https://doi.org/10.1177/7.1.69
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
MOTIVATION: 10x Genomics VisiumHD enables spatial transcriptomics at 2 µm × 2 µm resolution but exhibits slide-specific, non-periodic striping artifacts due to lane-width variability. These multiplicative row/column effects distort bin total counts and can bias downstream analyses. The state-of-the-art destriping approach is the normalization procedure used as a preprocessing step in bin2cell; it applies sequential high-quantile row- then column-wise normalization, which is asymmetric and can introduce edge effects/macro-stripes and distortions of large-scale total-count structure. RESULTS: We propose a statistical destriping approach that leverages nuclei segmentation from the co-registered H&E image. Assuming transcript abundance is constant within each nucleus, we model bin counts with a negative binomial distribution whose mean is a product of a nucleus-specific concentration and row- and column-specific stripe-factors reflecting lane-width variation. We fit all parameters in a generalized linear modeling framework with cross-validated regularization on stripe-factors and iterative dispersion estimation, and use the fitted parameters to correct the observed counts into a destriped image. On synthetic data with known ground truth, our method improves stripe-factor estimation accuracy and reduces error in corrected counts relative to bin2cell and bin2cell-derived baselines. Across four public VisiumHD slides, it consistently lowers striping intensity while substantially better preserving biological signal present in the large-scale global count structure and avoiding the artifacts introduced by other methods. AVAILABILITY AND IMPLEMENTATION: All source code and links to publicly available data used for this study are available at https://github.com/paolamalsot/destriping-GLM.
Explore the source record for details and available documents.
The contrast-to-noise ratio (CNR) was used to determine the detectability of objects within reconstructed images from diffuse near-infrared tomography. It was concluded that there was a maximal value of CNR near the location of an object within the image and that the size of the true region could be estimated from the CNR. Experimental and simulation studies led to the conclusion that objects can be automatically detected with CNR analysis and that our current system has a spatial resolution limit near 4 mm and a contrast resolution limit near 1.4. A new linear convolution method of CNR calculation was developed for automated region of interest (ROI) detection.