Weighted Image Generation for Medical Region Boundary Correction
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Solution Overview
Problem
Current methods for automatically correcting the boundary of regions of interest in medical images are prone to user variability and instability, leading to inaccurate region extraction and learning data, especially when using AI determiners for higher-quality 3D medical images.
Innovation Solution
A weighted image generation apparatus that displays medical images, allows for boundary correction based on user instructions, and generates weighted images with pixel values representing the certainty of being within the region of interest, using initial and corrected weight coefficients for accurate region definition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual operation is used to correct the boundary of the region of interest, then the boundary can be adjusted according to user intent, but the boundary setting becomes unstable and varies depending on the user and editing timing
Solution Approach 1:
The system generates a weighted image that provides visual feedback about the certainty of each pixel's inclusion in the region of interest. This feedback loop allows the system to automatically adjust boundaries based on the weight coefficients, reducing dependency on manual user input and ensuring consistent, reliable boundary setting across different users and editing timings.
Solution Approach 2:
The system performs automatic boundary correction using the weighted image generation, eliminating the need for manual user intervention. The automated process uses weight coefficients to naturally define boundaries, making the system self-sufficient and independent of user variability, thereby improving both accuracy and consistency.
2Measurement precision
If the boundary of the region of interest is corrected repeatedly to achieve accurate setting, then the boundary accuracy improves, but the time required for correction increases
Solution Approach 1:
The system performs preliminary boundary correction automatically by generating weighted images before the actual region extraction. This preliminary action establishes accurate boundaries in advance, eliminating the need for repeated manual corrections and significantly reducing the time required for final boundary setting.
Solution Approach 2:
The system replaces the mechanical manual correction process with an automated computational approach. Instead of requiring users to repeatedly adjust boundaries through manual operations, the system uses algorithms to automatically correct boundaries based on weight coefficients, thereby reducing correction time while maintaining high accuracy.
3Measurement precision
If AI determiners are trained using manually corrected region data, then the learning accuracy improves, but the process becomes complex and time-consuming
Solution Approach 1:
The system automatically generates the training data through weighted image generation, eliminating the need for manual annotation and correction processes. This self-service approach simplifies the training process by automatically producing high-quality learning data, reducing both the complexity and time required for AI determiner training while maintaining high learning accuracy.
Solution Approach 2:
The system replaces complex manual annotation and correction processes with automated computational algorithms. The weighted image generation process automatically creates training data from medical images, substituting the complex mechanical process of manual boundary correction with an automated system, thereby simplifying the overall training process while improving efficiency.
Data Source
AI summary
A display control unit displays a medical image from which at least one region of interest is extracted on a display unit. A correction unit corrects a boundary of the region of interest according to a correction instruction for the boundary of the region of interest extracted from the displayed medical image. An image generation unit generates a weighted image in which each pixel in the medical image has, as a pixel value of each pixel, a weight coefficient representing a weight of being within the region of interest, by setting an initial weight coefficient for the extracted region of interest and setting a corrected weight coefficient for a corrected region for which the correction instruction is given in the medical image.