Learning-Based Maxillofacial CT Segmentation for Similar Tissues
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image segmentation methods for medical images, particularly in CT scans, struggle with accurately distinguishing tissues with similar CT values, requiring human intervention to account for imaging conditions and individual differences.
Innovation Solution
A segmentation device using a learning model generated from training data to segment biologically important regions in maxillofacial images, such as blood vessels and mandibular canals, without human intervention, and calculate three-dimensional positional relationships, enabling improved accuracy and distance measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If mathematical segmentation methods based on CT values are used, then segmentation can be performed automatically, but segmentation accuracy deteriorates for tissues with close CT values
Solution Approach 1:
The patent introduces a learning model as an intermediary between the input image data and the segmentation output. This learning model, trained on annotated training data, acts as a mediator that captures complex relationships between imaging conditions, tissue characteristics, and segmentation results, thereby improving accuracy while maintaining automation.
Solution Approach 2:
The patent performs preliminary training of the learning model using annotated training data before actual segmentation. This preliminary action prepares the model to handle variations in imaging conditions and tissue characteristics, enabling accurate automatic segmentation without requiring manual intervention during the actual segmentation process.
2Measurement precision
If human intervention is used for segmentation determination, then segmentation accuracy improves by considering imaging conditions and individual differences, but productivity deteriorates due to manual work requirements
Solution Approach 1:
The patent enables the segmentation system to serve itself by automatically learning from training data and performing segmentation without human intervention. The learning model captures the expertise of human operators and applies it automatically, achieving both high accuracy and high productivity by eliminating the need for manual segmentation work.
3Measurement precision
If learning models are used for segmentation, then segmentation accuracy improves without human intervention, but device complexity increases
Solution Approach 1:
The patent replaces complex manual mechanical processes of human segmentation determination with an automated learning model. The learning model, once trained, provides a systematic and repeatable process that captures the complexity of human expertise without requiring human operators, thereby improving accuracy while the complexity is confined to the training phase rather than the operational phase.
Data Source
Figure 1
Figure 2
Figure 3
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 biologically important region is output when data of a constituent maxillofacial region is input.