Image-Based Tissue Microarray Region Selection for Standardized Layouts
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Solution Overview
Problem
The process of selecting cores and designing tissue microarray layouts in tissue microarrays is manual, time-consuming, and prone to variability, lacking automation and intelligence for efficient identification of relevant tissue cores.
Innovation Solution
An automated method for selecting candidate regions in tissue microarrays based on tissue composition analysis, using machine learning to determine distances between tissue compositions and target vectors, enabling intelligent selection and layout design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual core selection and TMA design methods are used, then flexibility and adaptability in selecting tissue regions are maintained, but the process becomes time-consuming and prone to human variability
Solution Approach 1:
The patent replaces manual mechanical selection of tissue cores with an automated computational image analysis system. Machine learning algorithms analyze histological images to identify and select candidate regions based on tissue composition, eliminating the need for manual visual inspection and selection by pathologists or technicians.
Solution Approach 2:
The system enables self-service automation where the TMA design process autonomously selects cores, determines optimal layouts, and generates construction protocols without continuous human intervention. The algorithm independently evaluates tissue regions, scores them based on predefined criteria, and makes selection decisions.
2Reliability
If manual core selection is performed, then human expertise can guide region selection, but sampling bias and human error increase perceived patient sample heterogeneity
Solution Approach 1:
The system incorporates feedback mechanisms where the image analysis algorithm continuously refines core selections based on tissue composition data. The system provides feedback on selection criteria, scores candidate regions, and adjusts selections to optimize representativeness and reduce sampling bias, ensuring consistent and reliable core selection across different samples.
Solution Approach 2:
The patent changes the parameters of core selection from subjective human judgment to objective quantitative metrics. The system uses computational parameters such as tissue composition percentages, region scores, and statistical measures to objectively evaluate and select cores, eliminating human error and improving reliability.
3Adaptability or versatility
If traditional manual TMA design methods are used, then customization for specific research questions is possible, but the process lacks standardization and intelligence
Solution Approach 1:
The system dynamically adapts to different research objectives by allowing users to configure selection criteria and parameters based on specific study requirements. The algorithm can be customized to prioritize different tissue compositions, region characteristics, or research questions while maintaining precise and intelligent selection through computational analysis.
Data Source
AI summary
Disclosed are systems comprising a processor; and a non-transitory computer readable medium comprising instructions that, when executed by the processor, cause the processor to obtain one or more images and determine one or more compositions of a plurality of regions within the one or more images. Additionally, systems disclosed herein determine a distance between the composition of the region and one or more target vectors for each region in the plurality of regions and select a candidate region from the plurality of regions.