Fiducial Marker Alignment for Spatial Analyte 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 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 edge detection algorithms to automate the alignment of sample images with spatial analyte data, allowing for accurate alignment of sample images with analyte data, even in cases where fiducial markers are obscured or repetitive.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alignment processes are used to align sample images with analyte data, then alignment accuracy can be maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system performs self-alignment by automatically detecting fiducial markers in the sample image and computing transformation parameters without human intervention. The alignment algorithm independently processes the image data, identifies marker positions, calculates transformation matrices, and applies corrections to align the sample image with the template, making the entire alignment process autonomous and eliminating manual labor while maintaining accuracy
Solution Approach 2:
The patent replaces manual mechanical alignment operations with an automated computational system. Instead of physically manipulating images or data through manual processes, the system uses computer vision algorithms to detect fiducial markers, mathematical transformations to compute alignment parameters, and automated image processing to apply corrections, thereby substituting mechanical human operations with electronic and computational processes
2Measurement precision
If fiducial markers are used for alignment, then alignment accuracy improves, but the system complexity increases due to marker design and detection requirements
Solution Approach 1:
The fiducial markers are designed with uniform visual characteristics - all markers share the same circular shape, size, and intensity properties. This homogeneity simplifies the detection algorithm by providing consistent features across all markers, allowing the system to use a single detection template and transformation model for all markers rather than handling multiple marker types with different detection strategies
Solution Approach 2:
The fiducial markers serve multiple functions simultaneously: they provide reference points for alignment, enable detection through their distinct visual features, and facilitate transformation calculation. The same marker design that makes them easy to detect also provides the geometric information needed for accurate alignment and transformation, eliminating the need for separate calibration or reference systems
3Productivity
If automated alignment algorithms are implemented, then productivity increases, but handling of obscured or repetitive markers becomes challenging
Solution Approach 1:
The alignment algorithm incorporates feedback mechanisms where the detected positions of fiducial markers are used to compute transformation parameters, which are then applied to align the sample image. The system can iterate this process, using the aligned image to re-detect markers and refine transformation parameters, thereby correcting errors from obscured or repetitive markers through iterative improvement based on feedback from each detection cycle
Solution Approach 2:
The system performs preliminary detection of all fiducial markers in the image before computing the final transformation parameters. By identifying all marker positions in advance and evaluating their reliability, the system can select the most reliable subset of markers for transformation calculation, or apply weighted approaches that account for marker quality, thereby preparing robust alignment parameters before final alignment execution
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.