Medical Support Device Lumen Position Identification
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Solution Overview
Problem
Existing medical imaging technologies face challenges in accurately determining the position of a lumen within medical images, particularly in distinguishing between central and non-central regions.
Innovation Solution
A medical support device equipped with a processor that inputs medical images into a trained model to generate certainty information for divided regions, allowing for the differentiation between cases where the lumen is present in central versus non-central regions based on positional relationships and threshold values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a trained model generates certainty information for divided regions to identify lumen position, then measurement precision is improved, but device complexity increases due to the need for region division and threshold comparison logic
Solution Approach 1:
The medical image is divided into multiple divided regions in the circumferential direction, and certainty information is generated for each region independently. This segmentation allows the system to identify the lumen position by comparing certainty values across regions, resolving the contradiction by improving measurement precision through region-specific analysis while managing complexity through systematic division rather than holistic complex processing
Solution Approach 2:
The patent introduces a new dimension of analysis by dividing regions in the circumferential direction and generating certainty information for each region. This dimensional transformation enables the system to distinguish between central and non-central lumen positions by comparing positional relationships and certainty values across multiple divided regions, thereby improving measurement precision without requiring fundamentally complex system architecture
2Measurement precision
If the system distinguishes between central and non-central lumen positions using positional relationships, then measurement precision is improved, but loss of information increases due to the need for threshold comparisons and case differentiation
Solution Approach 1:
The system uses threshold values as feedback mechanisms to determine whether certainty information indicates a central or non-central lumen position. By comparing certainty values against predefined thresholds and evaluating positional relationships between divided regions, the system maintains measurement precision while systematically managing information loss through structured decision-making logic that preserves essential positional and certainty data
Data Source
AI summary
A medical support method includes: causing a trained model to generate certainty information in which a certainty of a lumen being present in each of a plurality of divided regions obtained by dividing a medical image or an image corresponding to the medical image in a circumferential direction is given to the plurality of divided regions; outputting first information indicating that the lumen is present in any of the plurality of divided regions on the basis of the certainty information; and outputting second information indicating that the lumen is present in a central region of the medical image in a case where the certainty information is information in which a value exceeding a threshold value is given as the certainty to two or more divided regions having a positional relationship exceeding 90 degrees in the circumferential direction among the plurality of divided regions.