Tissue Microarray Sampling Protocol Optimization
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Solution Overview
Problem
There is a lack of standard methods for determining optimal tissue microarray (TMA) core sampling protocols, which affects the performance of machine learning models in medical image processing.
Innovation Solution
A system and method for determining TMA core sampling protocols by identifying a plurality of tissue cores from an image, selecting subsets based on candidate protocols, and evaluating these protocols using machine learning models to determine the best sampling approach for a given model and task.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If whole tissue section (WTS) slides are used for machine learning analysis, then abundant microenvironmental context is provided, but cost becomes prohibitive for multiplexed assays
Solution Approach 1:
The patent segments the tissue sample into multiple small cylindrical cores (tissue microarray cores) extracted from a larger tissue block. These cores are sectioned and placed into a well plate, allowing multiple patient samples to be stained simultaneously. This segmentation enables cost-effective multiplexed assays while preserving microenvironmental context through the organized arrangement of cores on the array.
Solution Approach 2:
The patent merges multiple tissue cores from different patients onto a single slide to form a tissue microarray. This allows simultaneous staining and analysis of multiple samples, amortizing the cost of experimental staining across multiple patients. The merged structure maintains individual core integrity while enabling economical batch processing.
2Quantity of substance
If tissue microarray cores are used to reduce cost, then staining cost scales with number of slides, but impact of sampling procedure on machine learning model performance is underexplored
Solution Approach 1:
The patent performs preliminary computational actions by creating synthetic tissue microarray datasets from whole slide images before actual physical sampling and staining. Virtual tissue cores are generated by extracting and sectioning digital representations of tissue regions, allowing evaluation of different sampling protocols in silico. This preliminary digital preparation enables systematic assessment of how sampling procedures affect downstream machine learning performance without physical constraints.
Solution Approach 2:
The patent creates synthetic copies of tissue microarrays by extracting digital images of tissue cores from whole slide images and assembling them into virtual TMA datasets. These synthetic TMAs replicate the physical sampling process computationally, enabling repeated experimentation and evaluation of different core sampling protocols to determine their impact on machine learning model performance.
3Adaptability or versatility
If different core sampling protocols are used, then flexibility in TMA design is increased, but lack of standard method makes optimization difficult
Solution Approach 1:
The patent systematically varies key parameters of the sampling protocol including core size (e.g., 0.5mm, 1.0mm, 1.5mm diameter), number of cores per patient, and spatial distribution patterns. By changing these parameters in controlled ways within the synthetic dataset generation process, the patent enables comparison of different sampling strategies while maintaining a standardized evaluation framework through consistent machine learning model training and performance metrics.
Data Source
AI summary
An exemplary method for determining a sampling protocol for sampling tissue cores for a tissue microarray includes obtaining an initial plurality of tissue cores from an image of a tissue slide; selecting a first subset of the initial plurality of tissue cores based on a first candidate sampling protocol; inputting the first subset of the plurality of tissue cores into a machine learning model; evaluating the first candidate sampling protocol by evaluating a first output of the machine learning model; selecting a second subset of the initial plurality of tissue cores based on a second candidate sampling protocol; inputting the second subset of the plurality of tissue cores into the machine learning model; evaluating the second candidate sampling protocol by evaluating a second output of the machine learning model; and determining the sampling protocol based on the evaluation of the first candidate sampling protocol and the second candidate sampling protocol.