Tissue Classification with Fiducial Bounding and Heuristic Voting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for tissue classification in biological images require human input, leading to inefficiencies, high labor costs, and susceptibility to errors, making them less effective for high-throughput applications and reducing the accuracy of spatial analyte analysis in complex tissues.
Innovation Solution
A method and system for automated tissue classification using fiducial markers, heuristic classifiers, and segmentation algorithms to distinguish tissue regions from background in images, allowing for reproducible identification without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual methods are used for tissue classification, then accuracy can be maintained through human judgment, but productivity is reduced and labor costs increase
Solution Approach 1:
The system enables automated self-classification of tissue regions through multiple heuristic classifiers that independently analyze image features and vote on classification decisions, eliminating the need for manual human intervention while maintaining consistent accuracy across high-throughput processing
Solution Approach 2:
The classification process is divided into multiple independent heuristic classifiers, each analyzing specific image features separately, with results aggregated through voting mechanisms to produce final classification decisions, enabling parallel processing of large datasets
2Reliability
If manual tissue classification is performed, then complex judgment can be applied, but loss of time increases due to human intervention requirements
Solution Approach 1:
Multiple heuristic classifiers perform preliminary analysis of different image features independently and simultaneously, with results aggregated through voting before final classification, enabling parallel processing that dramatically reduces overall processing time while maintaining comprehensive analysis quality
Solution Approach 2:
The system merges results from multiple independent heuristic classifiers through a voting aggregation mechanism, combining the strengths of different classification approaches to achieve high-quality results faster than any single manual method
3Productivity
If automated classification is implemented, then productivity increases, but device complexity increases due to multiple classifiers and algorithms
Solution Approach 1:
The complex classification task is segmented into multiple independent heuristic classifiers, each handling specific image features separately. This modular architecture enables parallel processing that increases productivity while keeping individual classifier components relatively simple and manageable
4Ease of operation
If human input is required for tissue classification, then ease of operation is maintained through simple interface, but loss of time increases due to manual processing requirements
Solution Approach 1:
The system performs automated self-classification through multiple heuristic classifiers that independently analyze and vote on tissue regions without requiring manual human input, dramatically reducing processing time while maintaining ease of operation through automated workflows
Solution Approach 2:
Multiple classifiers perform preliminary analysis simultaneously and aggregate results before final classification, enabling the system to process images quickly without requiring sequential manual review, thus reducing time loss while keeping the interface simple
Data Source
Figure 1
Figure 2
Figure 3A
AI summary
Systems and methods for tissue classification are provided. An image of tissue on a substrate is obtained as a plurality of pixels. Fiducial markers are on the substrate boundary. Pixels are assigned to a first class, indicating tissue sample, or a second class, indicating background. The assigning uses the fiducial markers to define a bounding box within the image and disregards pixels outside the box. Then, heuristic classifiers are applied to the pixels: for each respective pixel in the plurality of pixels, each heuristic classifier votes for the respective pixel between the first and second class, thereby forming an aggregated score for each pixel that in one of first class, likely first class, likely second class, and obvious second class. The aggregated score and intensity of each pixel is applied to a segmentation algorithm to assign a probability to each pixel of being tissue sample or background.