Medical Image Analysis via Hierarchical Lesion Data Positioning
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Solution Overview
Problem
Current medical image analysis for breast tissue images is highly subjective and varies significantly between clinicians, due to the low occurrence rate of cancers and the variability in interpreting diverse lesion types, shapes, and characteristics.
Innovation Solution
A method of analysis that involves receiving a breast tissue image, identifying regions of interest, positioning a query image within a hierarchy of lesion data based on tissue characteristics, retrieving statistics from neighboring images, generating analytics, and displaying graphical representations to aid in diagnostic decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If clinicians evaluate medical images based on subjective interpretation, then diagnostic flexibility is maintained, but diagnostic consistency and accuracy deteriorate due to high variability between clinicians
Solution Approach 1:
The patent introduces an intermediary system comprising a hierarchical graph structure and statistical analysis engine that mediates between the medical image data and clinician interpretation. This intermediary processes images through multiple hierarchical levels (super-lesion, lesion, sub-lesion) and provides statistical context about neighboring images, thereby standardizing evaluation while preserving clinician oversight and diagnostic flexibility.
2Measurement precision
If large-scale medical image data is analyzed to improve diagnostic accuracy, then detection capability improves, but computational complexity and data processing requirements worsen
Solution Approach 1:
The patent segments the large-scale medical image database into a hierarchical graph structure organized by anatomical regions, lesion types, and characteristics. This segmentation allows the system to analyze only relevant subsets of data (neighboring images with similar characteristics) rather than processing the entire database, thereby reducing computational complexity while maintaining high detection accuracy through targeted statistical analysis.
3Reliability
If diverse lesion types and characteristics are evaluated in detail, then diagnostic comprehensiveness improves, but analysis time and resource requirements worsen
Solution Approach 1:
The patent performs preliminary organization of medical images into a hierarchical graph structure during database construction, pre-computing relationships between images based on lesion characteristics, anatomical locations, and histological types. When a new image requires evaluation, the system quickly identifies neighboring images at the same hierarchical level without performing comprehensive analysis of all stored images, thereby reducing analysis time while maintaining diagnostic comprehensiveness.
Data Source
AI summary
A system and method of analysis for medical image data. An image of breast tissue is received, a region of interest (ROI) in the image is identified based on tissue characteristics, and a query image is defined. A hierarchy of lesion data is retrieved, the hierarchy being formed based on one or more relationships among a plurality of images, and the query image is positioned within the hierarchy of lesion data based on the tissue characteristics. A position of the query image within the hierarchy of the lesion database is determined, identifying one or more neighbor images of the query image from among the plurality of images based on the position, and statistics associated with one or more neighbor images are retrieved. Analytics associated with the query image based on the statistics are generated, and a graphic depicting the analytics is displayed.