MRI Lesion Segmentation via Composite Probability Maps
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Solution Overview
Problem
Current methods for automated segmentation of lesions in MRI images are sub-optimal, requiring manual corrections and subjective assessments, leading to variability and inefficiency in clinical settings, especially for multiple sclerosis patients.
Innovation Solution
A system that generates multiple probability maps from MRI images, combining them to create a composite map that objectively identifies lesions, reducing the need for manual corrections by leveraging data from large patient populations and anatomical atlases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated segmentation methods are used, then productivity is improved, but reliability deteriorates due to sub-optimal performance and need for manual corrections
Solution Approach 1:
The patent combines multiple probability maps (lesion probability map from current image, longitudinal probability map from historical images, and generic probability map from population data) into a composite probability map. This merging of multiple information sources resolves the contradiction by maintaining high productivity through automation while improving reliability through comprehensive data integration and cross-validation.
Solution Approach 2:
The composite probability map acts as a composite structure integrating different types of probability information (current scan, historical scans, population norms). This composite approach enables automated segmentation to achieve both high productivity and high reliability by leveraging diverse data sources rather than relying on a single method.
2Reliability
If manual corrections are performed, then reliability is improved, but loss of time increases due to subjective assessment variability
Solution Approach 1:
The system performs self-correction by automatically integrating multiple probability maps and using population-based generic maps to correct individual scan anomalies. This self-service mechanism eliminates the need for manual corrections while maintaining reliability, thus resolving the contradiction between measurement consistency and analysis time.
Solution Approach 2:
The longitudinal probability map provides feedback from historical patient data to improve current lesion identification. This feedback loop enables the system to automatically adjust and correct without manual intervention, achieving both reliability and time efficiency.
3Reliability
If population data is integrated, then reliability is improved through standardized analysis, but device complexity increases
Solution Approach 1:
The patent segments the probability integration into distinct components: lesion probability map, longitudinal probability map, and generic probability map. This segmentation of complex data processing into manageable modules resolves the contradiction by enabling standardized analysis through population data while organizing complexity into structured, manageable components.
Data Source
AI summary
Systems and methods are provided for automated segmentation of lesions within a region of interest of a patient. At least one magnetic resonance imaging (MRI) image of the region of interest is produced. At least one probability map is generated from the at least one MRI image. A given probability map represents, for each of a plurality of pixels, a likelihood that a lesion is present at the location represented by the pixel given the at least one MRI image of the region of interest. The at least one probability map is combined with a plurality of additional probability maps to provide a composite probability map. Lesions are identified from the composite probability map.