Thorax X-Ray Regional Quantification for Automated Lung Disease Staging
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Solution Overview
Problem
Current medical diagnostic imaging for lung diseases, such as COVID-19, relies heavily on visual inspection and verbal description by radiologists, lacking automation for disease quantification.
Innovation Solution
A computer-implemented system that automatically evaluates thorax X-ray images by registering them to anatomical structures, identifying regions of interest, generating feature distributions, and analyzing these to provide quantitative diagnostic outputs, including disease staging and progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If visual inspection and verbal description by radiologists is used, then diagnostic accuracy is maintained, but automation level is low and productivity is limited
Solution Approach 1:
The system segments the lung field into multiple regions of interest (e.g., upper, middle, lower zones) and analyzes each region separately. This segmentation allows the automated system to process complex imaging data in manageable portions, achieving automation while maintaining diagnostic precision through region-specific analysis.
Solution Approach 2:
The system introduces an intermediary computational layer between the raw X-ray image and the diagnostic conclusion. This intermediary layer includes image registration, feature extraction, and quantification modules that automatically process the imaging data, enabling automation without requiring direct human interpretation of all complex features.
2Productivity
If automated disease quantification is implemented, then productivity is improved, but measurement precision may be compromised
Solution Approach 1:
The system replaces the mechanical/subjective process of visual inspection with an automated computational system. It uses image registration algorithms, feature extraction techniques, and quantitative analysis to automatically measure disease parameters, thereby increasing productivity while maintaining precision through objective, reproducible measurements.
Solution Approach 2:
The system transforms qualitative radiological findings into quantitative parameters. By converting visual assessments into measurable values (e.g., lung opacity percentages, region-specific scores), the system enables automated processing while maintaining diagnostic precision through rigorous parameter-based analysis.
3Measurement precision
If regional analysis is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system divides the lung field into multiple regions of interest and analyzes each region separately. This segmentation enables precise regional quantification of disease severity while managing processing complexity by handling each region independently through standardized analysis protocols.
Solution Approach 2:
The system applies different analysis criteria and weighting to different regions based on their specific characteristics and clinical relevance. This local quality approach allows for precise regional measurement while optimizing processing efficiency by tailoring analysis parameters to each region's specific requirements.
Data Source
AI summary
Systems, apparatuses and methods provide technology to automatically evaluate diagnostic images, including receiving a diagnostic image relating to a condition of a patient, performing a registration of the diagnostic image with reference to an anatomical structure, identifying one or more ROIs from the registered image, generating a feature distribution based on the one or more ROIs, analyzing the feature distribution to determine a quantification, the quantification reflecting the condition of the patient, and providing a diagnostic output based on the quantification. In embodiments, identifying a ROI includes identifying a field of interest in the registered image, the field of interest encompassing the one or more regions of interest, and dividing the field of interest into a plurality of sub-regions. In embodiments, generating a feature distribution includes generating an intensity histogram for each ROI. In embodiments, analyzing the feature distribution includes determining one or more metrics based on the feature distribution.