Brain Image Segmentation for Objective Disease Indexing
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Solution Overview
Problem
Existing medical image analysis methods rely heavily on clinical judgment, leading to subjective disease information and variability due to differing image acquisition conditions, making accurate diagnosis assistance difficult.
Innovation Solution
An image analysis device using a highly trained artificial neural network to segment medical images, distinguishing ventricle and white matter hyperintensity regions, and calculating disease indices within and outside a predetermined reference boundary to provide objective diagnosis assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a doctor's clinical judgment is used to analyze medical images, then diagnostic information can be obtained, but the information becomes subjective and varies between doctors
Solution Approach 1:
The patent replaces the mechanical system of human clinical judgment with an automated image analysis system that processes medical images objectively. The system segments images into anatomical regions, quantifies disease features, and generates standardized reports, eliminating subjective variability while maintaining diagnostic accuracy through systematic analysis.
Solution Approach 2:
The patent transforms qualitative clinical judgment into quantitative parameters by measuring specific image features such as lesion area, density, and distribution. By converting subjective diagnostic criteria into objective measurable parameters, the system ensures consistent results across different cases and operators.
2Measurement precision
If image segmentation is performed to provide disease information, then diagnostic accuracy improves, but the process becomes cumbersome when individually segmenting medical images obtained under various conditions
Solution Approach 1:
The patent creates a universal segmentation framework that automatically adapts to different medical image acquisition conditions. The system identifies anatomical structures and disease features across varying imaging parameters without requiring manual re-segmentation, making the process robust to changes in imaging protocols while maintaining consistent diagnostic measurements.
Solution Approach 2:
The system performs self-adjustment by automatically detecting image characteristics and adapting segmentation parameters accordingly. Rather than requiring manual intervention for each image condition, the algorithm autonomously optimizes segmentation based on the specific image properties, reducing operational complexity.
3Loss of information
If regions are segmented from medical images to provide disease information, then diagnostic value increases, but sufficient information cannot be obtained when images are acquired under varying conditions
Solution Approach 1:
The patent compensates for variability in two-dimensional medical images by introducing a standardized three-dimensional anatomical reference framework. By mapping segmented regions to this reference model, the system recovers complete disease information even when individual images are acquired under suboptimal conditions, ensuring comprehensive diagnostic data.
Data Source
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AI summary
The present invention relates to a method of providing diagnosis assistance information by analyzing a medical image, the method including obtaining an image of the brain, labeling a feature value representing a region of the brain, determining a reference boundary in the image of the brain, calculating a first disease index and a second disease index, and providing diagnosis assistance information on the basis of the first and second disease indexes.