Tissue Microarray Spot Registration via Segmentation
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Solution Overview
Problem
The alignment of tissue microarrays (TMAs) made from serial sections of a multiple-tissue sample block is difficult due to non-rectilinear grids and frequent loss of tissue cores, making it challenging to correlate samples between slides for comparative analysis.
Innovation Solution
A method and system for registering and analyzing images of TMAs to determine the position and correspondence of tissue spots across multiple slides, allowing for the generation of an output that relates tissue spots to each other and clinical information, using either whole slide images or relative x-y coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If individual tissue cores are imaged manually or automatically on TMAs, then imaging flexibility and adaptability are improved, but grid rectilinearity deteriorates and alignment between slides becomes difficult
Solution Approach 1:
The patent segments the TMA slide into multiple individual tissue core images, each processed and aligned independently. The system then reassembles these segmented images into a coherent grid structure, maintaining imaging flexibility while restoring grid rectilinearity through computational alignment algorithms.
Solution Approach 2:
The patent replaces manual mechanical alignment procedures with automated image processing algorithms. The system uses computational methods to register and align individual tissue core images, eliminating the need for manual grid adjustment and restoring rectilinear geometry through digital transformation rather than physical manipulation.
2Measurement precision
If tissue cores are manually aligned on slides, then alignment precision is improved, but time consumption and labor requirements increase
Solution Approach 1:
The patent implements self-service alignment where the system automatically registers and aligns tissue core images without requiring manual intervention. The automated algorithms independently perform the alignment task, achieving precision comparable to manual methods while eliminating the time and labor costs associated with manual alignment procedures.
Solution Approach 2:
The patent substitutes manual mechanical alignment operations with automated computational algorithms. The system uses image processing techniques to automatically register tissue core positions across multiple slides, achieving precise alignment through digital computation rather than manual physical adjustment, thereby reducing time consumption while maintaining precision.
3Adaptability or versatility
If deformable mesh grid approach is used, then grid adaptability to tissue layout is improved, but requirement for whole slide image and user intervention increases
Solution Approach 1:
The patent segments the TMA into individual tissue core images, processes them independently, and then reassembles them into a standardized grid. This segmentation approach provides grid adaptability to various tissue layouts while avoiding the need for complex deformable mesh algorithms and whole slide images, reducing overall system complexity.
Solution Approach 2:
Instead of starting with a whole slide image and applying deformable mesh correction, the patent inverts the approach by first extracting individual tissue core images and then assembling them into a standardized grid. This inversion eliminates the need for deformable mesh algorithms and whole slide imaging, reducing system complexity while maintaining grid adaptability.
4Extent of automation
If image-based approaches are used to identify tissue cores, then automation is improved, but requirement for linear grid structure and correlation with clinical information is lost
Solution Approach 1:
The patent segments tissue core identification from the imaging process, extracting individual cores and their positions independently. This segmentation enables automated processing while preserving the ability to correlate each core with clinical information through maintained metadata associations, overcoming the limitation of image-based approaches that lose correlation capabilities.
Solution Approach 2:
The patent introduces an intermediary data structure that maintains the relationship between automated image processing and clinical information correlation. This intermediary layer preserves metadata associations and enables correlation with clinical data while allowing full automation of the image processing and tissue core identification steps.
Data Source
AI summary
The present techniques include methods and systems for finding correspondences between tissue spots in tissue microarray serial sections belonging to the same recipient block. The present techniques may also be used to relate individual tissue cores to clinical information. Using either a whole slide image or the relative x-y coordinates of the tissue spots on the slide, individual tissue spots in different tissue microarrays may be linked to one another and their clinical information.