TMA Core Boundary Detection Using Gaussian Localization

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Solution Overview

Problem

Current TMA technologies face challenges in automated high-speed analysis, particularly in reliable automatic gridding and TMA core boundary detection, which are essential for accurate and objective analysis of tissue micro-arrays, especially when dealing with overlapping cores.

Innovation Solution

A method and system for automated quantitation of TMA digital image analysis that includes contrast adjustment, Gaussian kernel-based center localization, digital filtering to remove artifacts, and boundary determination to accurately identify and separate TMA cores, enabling reliable automatic gridding and core boundary detection.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated high-speed analysis is implemented for TMA, then productivity is improved, but measurement precision deteriorates due to unreliable gridding and boundary detection

Engineering Contradiction:

Improveanalysis speedVSAvoidboundary detection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The analysis process is divided into distinct sequential stages: contrast adjustment to enhance core visibility, Gaussian kernel-based center localization to precisely identify core positions, digital filtering to remove artifacts, and boundary determination to define core extents. This segmentation allows each stage to optimize for its specific function while maintaining overall automation and precision.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The method performs preliminary contrast adjustment and center localization before boundary detection. By pre-processing the image to enhance contrast and accurately locate centers using Gaussian kernels, the system prepares optimized input data for subsequent boundary detection algorithms, improving both speed and accuracy of the final measurement.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If manual analysis methods are used for TMA, then measurement precision is maintained, but productivity deteriorates due to time-consuming processing

Engineering Contradiction:

Improveanalysis accuracyVSAvoidanalysis throughput

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system performs self-service through automated image processing algorithms that independently complete contrast adjustment, center localization, artifact filtering, and boundary detection without manual intervention. The Gaussian kernel-based center localization and digital filtering operations enable the system to automatically correct and enhance images, maintaining precision while eliminating time-consuming manual steps.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical analysis operations are replaced with computational algorithms. The Gaussian kernel convolution operation substitutes manual boundary tracing, while digital filtering algorithms replace manual artifact removal. This substitution maintains measurement precision through mathematical rigor while dramatically increasing analysis throughput through automated computation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Device complexity

If simple image processing is applied, then device complexity is reduced, but measurement precision deteriorates due to inability to handle overlapping cores

Engineering Contradiction:

Improveprocessing algorithm complexityVSAvoidcore separation accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The system employs dynamic adaptive processing where the complexity of operations adjusts based on image characteristics. Gaussian kernel-based center localization dynamically identifies core positions regardless of overlap程度, and boundary determination algorithms adaptively adjust to separate overlapping cores. This dynamic approach handles complex overlapping scenarios while maintaining reasonable algorithmic complexity through iterative refinement rather than overly complex initial algorithms.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS8068988B2Method for automated processing of digital images of tissue micro-arrays (TMA)

Publication Date: 2011.11.29 VENTANA MEDICAL SYSTEMS INC

AI summary

A method and system for automated quantitation of tissue micro-array image (TMA) digital analysis. The method and system automatically analyze a digital image of a TMA with plural TMA cores created using a needle to biopsy or other techniques to create standard histologic sections and placing the resulting needle cores into TMA. The automated analysis allows a medical conclusion such as a medical diagnosis or medical prognosis (e.g., for a human cancer) to be automatically determined. The method and system provides reliable automatic TMA core gridding and automated TMA core boundary detection including detection of overlapping or touching TMA cores on a grid.