Endoscope Lumen Localization Using Circumferential Confidence Mapping
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Solution Overview
Problem
Existing medical imaging technologies struggle to accurately ascertain the position of lumens in medical images, particularly in luminal organs like the large intestine, which hinders precise medical procedures.
Innovation Solution
A medical support device and method that utilizes a trained model to generate confidence levels for divided regions in medical images, determining a lumen existence region with higher accuracy by summing weighted vectors based on these levels, and displays this information superimposed on the image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a medical image is divided into multiple regions for analysis, then the measurement precision of lumen position can be improved, but the device complexity increases due to the need for multiple processing steps and confidence level calculations
Solution Approach 1:
The medical image is divided into multiple divided regions along the circumferential direction, with each region assigned a confidence level indicating the likelihood of lumen presence. This segmentation enables precise lumen position detection by analyzing specific regions rather than the entire image, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The system changes the parameter representation from binary presence/absence to confidence levels ranging from 0 to 1 for each divided region. This parameter transformation allows for more nuanced lumen position determination while maintaining manageable processing complexity through standardized confidence level calculations.
2Measurement precision
If confidence levels are calculated for multiple divided regions, then the lumen existence region can be identified with higher accuracy, but the processing time increases
Solution Approach 1:
The image is pre-divided into multiple regions along the circumferential direction before lumen detection, and confidence levels are calculated for each region in parallel. This preliminary segmentation and parallel processing approach enables accurate lumen existence region identification while minimizing processing time through efficient region-based analysis.
3Manufacturing precision
If the lumen existence region is specified with higher resolution along the circumferential direction, then the medical intervention precision is improved, but the difficulty of detecting and measuring increases due to the need for vector summation and weight calculations
Solution Approach 1:
The system replaces complex geometric analysis with vector-based calculations, where direction vectors are assigned to divided regions and weighted by confidence levels. The lumen existence region is determined by summing these weighted vectors, which simplifies the measurement process while achieving high-resolution circumferential positioning for precise medical interventions.
Data Source
AI summary
A medical support device includes a processor. The processor is configured to input a medical image generated by imaging an inside of a luminal organ including a lumen to a trained model to generate a plurality of confidence levels that correspond to a plurality of divided regions obtained by dividing the medical image or an image corresponding to the medical image along a circumferential direction, and that indicate that the lumen is shown in the plurality of divided regions. In addition, the processor is configured to output lumen specification information for specifying a lumen existence region in which an existence position of the lumen is specified with higher accuracy than in the divided regions in the medical image or the image corresponding to the medical image, based on the plurality of divided regions and the plurality of confidence levels.