3D Image Reference Cross-Section Estimation
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Solution Overview
Problem
Existing methods for estimating reference cross-sections in three-dimensional medical images often rely on manual settings and are prone to errors, especially when image quality is low in peripheral regions.
Innovation Solution
An image processing apparatus and method that acquire a three-dimensional image, estimate intersecting cross-sections and intersecting line information, and update reference cross-section parameters based on this information to improve estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual settings are used to search for anatomical landmarks in three-dimensional spaces, then reference cross-sections can be obtained, but the burden on doctors increases significantly
Solution Approach 1:
The system performs automatic reference cross-section estimation by acquiring multiple intersecting cross-sections and computing their intersection lines, eliminating the need for manual landmark search by doctors while maintaining high estimation accuracy
Solution Approach 2:
The system performs preliminary acquisition of multiple intersecting cross-sections and their intersection lines before final reference cross-section determination, enabling automatic estimation without manual intervention
2Ease of operation
If automatic estimation based on rough estimates is used, then the burden on doctors is reduced, but estimation accuracy may be insufficient
Solution Approach 1:
The system transitions from two-dimensional rough estimate correction to three-dimensional automatic estimation by acquiring multiple intersecting cross-sections and computing their intersection lines in 3D space, achieving both ease of operation and high accuracy
Solution Approach 2:
The system uses intersection lines of multiple intersecting cross-sections as intermediaries to automatically determine the reference cross-section, replacing manual landmark search while maintaining high estimation accuracy
3Device complexity
If a prescribed start point is set and reference cross-section is calculated based on peripheral information, then the process is simplified, but satisfactory estimation cannot be achieved when peripheral image quality is low
Solution Approach 1:
The system segments the three-dimensional image into multiple intersecting cross-sections and uses their intersection lines to determine the reference cross-section, avoiding dependence on peripheral region quality around a single start point
Solution Approach 2:
The system acquires multiple intersecting cross-sections that can serve as alternative sources for reference cross-section estimation, making the method robust to image quality variations in any single region
Data Source
AI summary
An image processing apparatus includes a processor; and a memory storing a program which, when executed by the processor, causes the image processing apparatus to: an image acquisition processing to acquire a three-dimensional image containing a subject as an object to be imaged, an intersecting cross-section acquisition processing to acquire, from the three-dimensional image, information on a plurality of intersecting cross-sections that intersect with a prescribed reference cross-section, an intersecting line information acquisition processing to, on a basis of the information on the plurality of intersecting cross-sections, acquire intersecting line information that represents information on intersecting lines where the plurality of intersecting cross-sections intersect with the reference cross-section, and a cross-section information acquisition processing to, on a basis of the intersecting line information, acquire reference cross-section information that represents information on the reference cross-section.