Robust Object Segmentation Confidence Mapping for Microscopy
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Solution Overview
Problem
Current high content image analysis methods in microscopy introduce measurement variations and errors due to segmentation issues, leading to reduced signal dynamic range and assay variability, which complicates the evaluation of assay quality in high throughput and high content screening.
Innovation Solution
The implementation of robust object segmentation confidence mapping and confidence-based measurements, along with feature extraction at various levels of cellular analysis, to reduce measurement variations and improve repeatability, allowing for more accurate and specific analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image analysis methods are used, then the analysis process is simple, but measurement precision and reliability are reduced due to segmentation errors
Solution Approach 1:
The patent applies segmentation by dividing the image analysis into multiple confidence levels (high confidence, medium confidence, low confidence regions) and processing them separately. This allows precise measurement in high confidence regions while maintaining overall system simplicity through automated classification of region reliability.
Solution Approach 2:
The patent implements local quality by assigning different processing standards to different spatial regions based on segmentation confidence. High confidence regions receive precise measurement treatment, while medium and low confidence regions are handled with appropriate tolerance levels, optimizing measurement precision where it matters most without unnecessarily complicating the entire analysis system.
2Reliability
If high content analysis is performed, then detection sensitivity is improved, but assay variability increases due to segmentation issues
Solution Approach 1:
The patent implements feedback by using segmentation confidence maps to guide subsequent measurement operations. The confidence assessment from one processing stage feeds back to determine the appropriate measurement precision and validation criteria for subsequent stages, reducing assay variability through adaptive quality control that maintains high detection sensitivity while ensuring reliable measurements only where segmentation is confident.
3Productivity
If automated image analysis is used, then productivity is improved, but measurement errors increase due to lack of manual verification
Solution Approach 1:
The patent applies self-service by enabling the automated system to self-validate its own measurements through confidence-based quality control. The system automatically identifies low confidence regions and performs self-correction or flags them for review, maintaining high productivity through automation while preserving measurement precision through built-in quality assurance mechanisms that eliminate the need for complete manual verification.
Data Source
AI summary
An object analysis method performs object segmentation to generate segmentation confidence map and uses the segmentation results to generate robust object features. The robust object features are combined to create robust FOV summary features, robust sample summary features and robust assay summary features.