Coded Fiducial Markers for Spatial Analyte Image Registration
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Solution Overview
Problem
Existing spatial analysis methods face challenges in accurately aligning sample images with analyte data due to imperfections in tissue sample preparation and imaging, leading to uncertainties and labor-intensive manual alignment processes.
Innovation Solution
A method utilizing fiducial markers with unique N-digit codes and concentric patterns for automated alignment, enabling precise registration of sample images with capture spots, even in cases where fiducial markers are obscured or repetitive, by employing edge detection and alignment algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual alignment processes are used to align sample images with analyte data, then flexibility and adaptability are maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system performs automated self-alignment by detecting fiducial markers in the sample image and computationally transforming the image to align with the capture spot array, eliminating the need for manual operator intervention while maintaining alignment accuracy
Solution Approach 2:
The patent replaces manual mechanical alignment operations with an automated computational image transformation system that uses fiducial marker detection and coordinate transformation algorithms to achieve precise alignment
2Measurement precision
If traditional fiducial markers are used, then alignment can be achieved, but accuracy deteriorates when markers are obscured or repetitive patterns exist
Solution Approach 1:
The patent employs asymmetric concentric annular patterns with non-uniform spacing between rings, creating unique fiducial markers that are distinguishable even when repetitive, and maintains reliability under obscuration by using multiple asymmetric features within each marker
Solution Approach 2:
The system transitions from detecting simple 2D marker positions to detecting the full geometric structure of concentric annular patterns, using radial and angular information from multiple rings to robustly determine marker locations and orientations even when partially obscured
3Productivity
If automated alignment algorithms are implemented, then productivity and reproducibility improve, but system complexity increases
Solution Approach 1:
The patent incorporates fiducial markers into the substrate design before sample processing, pre-establishing the alignment reference framework that enables automated detection and transformation without requiring complex real-time adjustments
Solution Approach 2:
The system uses fiducial markers as intermediary reference objects between the physical substrate and the digital image coordinate system, simplifying the alignment problem into a marker detection and transformation task rather than requiring direct feature matching between sample and capture spots
4Measurement precision
If unique N-digit codes are assigned to each fiducial marker, then alignment precision improves, but the complexity of marker design and manufacturing increases
Solution Approach 1:
The patent encodes unique identifiers by varying physical parameters of the concentric annular patterns, specifically the number of rings, their radial positions, and spacing intervals, allowing high-information-content markers to be manufactured using standard deposition techniques without requiring complex multi-material processes
Data Source
AI summary
Systems and methods for spatial analysis of analytes are provided. A data structure is obtained comprising an image, as an array of pixel values, of a sample on a substrate having intersecting border regions, fiducial markers encoding N-digit codes, and a set of capture spots, where at least two border regions includes a fiducial marker. The pixel values are analyzed to identify locations of fiducial markers. The locations are aligned with locations of reference fiducial markers in a template using an alignment algorithm to obtain a final transformation between the fiducial markers in the image and the reference fiducial markers in the template. The final transformation and a coordinate system of the template are used to register the image to the set of capture spots. The registered image is then analyzed in conjunction with spatial analyte data associated with each capture spot, thereby performing spatial analysis of analytes.


