EMDQ: Removal of Image Feature Mismatches in Real-Time

Zhou, Huajun; Jayender, Jagadeesan

doi:10.1109/tip.2021.3134456

Cited by 5 publications

(1 citation statement)

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“…The computed field describes displacement distribution of each current section’s adjacent section(s) compared to its own. As the exerted force which causes the displacement passes through the whole section of tissue, the field value is expected to change smoothly across different locations[48][49][50]. Our method makes use of this property, to reduce errors in the field induced by both raw data defection and inaccuracies caused by upper stream algorithms.…”

Section: Methodsmentioning

confidence: 99%

ST-GEARS: Advancing 3D Downstream Research through Accurate Spatial Information Recovery

Xia,

Hu,

Zuo

et al. 2023

Preprint

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Three-dimensional Spatial Transcriptomics has revolutionized our understanding of tissue regionalization, organogenesis, and development. However, to reconstruct single sections back to theirin situthree-dimensional morphology, existing approaches either only adopt gene expression information to guide reconstruction or overlook shape correction against experiment-induced section distortions. This leads to significant discrepancies between reconstruction results and the actualin vivolocations of cells, imposing unreliable spatial profiles to downstream analysis. To address these challenges, we propose ST-GEARS (Spatial Transcriptomics GEospatial profile recovery system through AnchoRS), which solves optimized ‘anchors’ betweenin situclosest spots utilizing expression and structural similarity across sections and recoversin vivospatial information under the guidance of anchors. By employing innovative Distributive Constraints into the Optimization scheme, it retrieves anchors with higher precision compared to existing methods. Taking these anchors as reference points, ST-GEARS first rigidly aligns sections, then introduces and infers Elastic Fields to counteract distortions. ST-GEARS denoises the fields using context information by Gaussian Denoising. Utilizing the denoised fields, it eliminates distortions and eventually recovers original spatial profile through innovative and mathematically proved Bi-sectional Fields Application. Studying ST-GEARS on both bi-sectional registration and complete tissue reconstruction across sectional distances and sequencing platforms, we observed its outstanding performance in spatial information recovery across tissue, cell, and gene levels compared to current approaches. Through this recovery, ST-GEARS provides precise and well-explainable ‘gears’ betweenin vivosituations and 3Din vitroanalysis, powerfully fueling the potential of biological discoveries.

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Section: Methodsmentioning

confidence: 99%