Biological Sample Image Alignment With Manual Glyph Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for spatial analysis of biological samples face challenges in accurately aligning images of biological samples with fiducial patterns and distinguishing sample regions from background, compounded by sample imperfections and handling issues, leading to inaccurate data alignment and increased background noise.
Innovation Solution
A system and method for aligning images of biological samples with fiducial patterns using glyph coordinates, allowing manual adjustment for precise alignment, and identifying sample regions through user input to reduce background noise and improve data resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated techniques are used to determine the reference frame of fiducials in images, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary automated alignment to establish an initial reference frame, then allows for subsequent manual refinement. This preliminary action captures the bulk of alignment tasks automatically while reserving the option for precision adjustments when needed.
Solution Approach 2:
The alignment system transitions from a static automated approach to a dynamic hybrid approach. The reference frame determination can operate in automated mode for routine cases and switch to manual adjustment mode when precision requirements demand it, making the system adaptable to different precision needs.
2Measurement precision
If manual adjustment of reference frame is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The automated alignment system performs the initial reference frame determination without human intervention, serving itself for the routine alignment task. This eliminates the need for manual adjustment in cases where automated alignment suffices, saving time while maintaining adequate precision.
Solution Approach 2:
The system provides feedback mechanisms that allow users to assess the quality of automated alignment results. If the automated reference frame determination meets precision requirements, no further action is needed. If not, the feedback triggers manual adjustment, ensuring precision is only improved when necessary.
3Quantity of substance
If background regions are included in spatial analysis, then quantity of data is improved, but object-affected harmful factors increase
Solution Approach 1:
The system extracts and separates background regions from the biological sample regions in the image. By identifying and extracting the background portion, it can be excluded from subsequent spatial analysis, preventing background noise from contaminating the analyte data while preserving the integrity of the sample data.
Solution Approach 2:
Different regions of the image are assigned different qualities or statuses. The biological sample regions are marked for inclusion in analysis with high priority, while background regions are marked for exclusion. This local differentiation allows the system to process only relevant data, maintaining data quality while managing volume effectively.
Data Source
AI summary
Systems and methods for evaluating a biological sample on a substrate are provided. An image of the biological sample and glyphs on the substrate are displayed on a display as a plurality of pixels. Respective indications are received of coordinates within the image of the glyph locations. These and a reference fiducial pattern that includes the plurality of glyphs are used to calculate and display an initial alignment between the image and the fiducial pattern. The alignment is updated through manual user adjustments to glyph coordinates. A set of pixels in the plurality of pixels depicting the biological sample are received from a user. Identification of each capture spot in a plurality of capture spots encompassed by the set of pixels is outputted to an output file, with each respective capture spot being identified within the image for the output file based on the updated alignment.