Medical Image Segmentation Using Multi-Parameter Learning Model
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Solution Overview
Problem
Current medical image segmentation techniques, particularly for CT scans, face challenges in accurately segmenting tissues with close CT values, requiring human intervention and struggling with individual differences and imaging conditions, which limits their accuracy and efficiency.
Innovation Solution
A segmentation device and method using a learning model generated from training data that includes projection and reconfiguration data from X-ray CT or MRI scans, enabling the automatic segmentation of biological and artificial features in maxillofacial regions, such as teeth and bones, without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mathematical segmentation based on CT values is used, then the segmentation process is simple and fast, but segmentation accuracy deteriorates for tissues with close CT values
Solution Approach 1:
The patent transforms the segmentation approach from relying solely on CT value parameters to incorporating multiple parameters including position information, shape characteristics, and texture features. This multi-parameter integration allows the system to distinguish between tissues with similar CT values by considering their spatial relationships and structural properties rather than depending on a single threshold-based parameter.
2Measurement precision
If human intervention is introduced for segmentation, then segmentation accuracy improves, but the complexity and time consumption increase
Solution Approach 1:
The patent implements a self-service mechanism where the segmentation system automatically performs complex multi-parameter analysis and decision-making without requiring human intervention. The system independently evaluates position, shape, texture, and CT value characteristics to autonomously distinguish between different tissues, thereby achieving high accuracy while maintaining operational simplicity and avoiding the complexity associated with manual review processes.
3Measurement precision
If human intervention is required for segmentation, then segmentation accuracy improves, but processing time increases
Solution Approach 1:
The patent replaces the mechanical human review process with an automated computational system that performs multi-parameter analysis. The system uses algorithms to evaluate position, shape, texture, and CT value characteristics simultaneously, achieving segmentation accuracy previously attainable only through human intervention while dramatically reducing processing time by eliminating manual review steps.
4Adaptability or versatility
If segmentation is performed on tissues with close CT values, then comprehensive tissue analysis is achieved, but segmentation accuracy deteriorates
Solution Approach 1:
The patent applies segmentation principles by dividing the analysis into multiple independent feature evaluations (position, shape, texture, CT value) rather than relying on a single integrated assessment. This allows the system to separately evaluate each characteristic and combine the results, enabling accurate distinction between tissues with similar CT values through their unique combinations of positional, structural, and textural properties.
Data Source
AI summary
A learning model provided in a segmentation device is a learning model which is generated using training data such that segmentation data of a feature region is output when at least one of projection data and reconfiguration data acquired by an imaging device or data derived from the at least one of projection data and reconfiguration data is input.