Endoscope Lumen Direction Detection via Trained Model
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Solution Overview
Problem
Current endoscope insertion direction detection technologies face challenges in accurately determining the lumen direction, leading to potential unnecessary burdens on patients due to incorrect insertion directions, which can be difficult for medical professionals to recognize during procedures.
Innovation Solution
An image processing device utilizing a trained model through machine learning to determine the lumen direction by analyzing the positional relationship between division regions and a lumen corresponding region in endoscopic images, providing accurate lumen direction information for improved insertion guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional endoscope insertion direction detection methods are used, then the device complexity is reduced, but the measurement precision of lumen direction is insufficient
Solution Approach 1:
The patent introduces a trained machine learning model as an intermediary between the endoscopic image and the lumen direction determination. The model processes the image data and outputs direction information, acting as a mediator that transforms complex image analysis into actionable directional guidance without requiring complex manual processing systems
Solution Approach 2:
The patent replaces conventional mechanical or rule-based image processing methods with a machine learning-based system. The trained model automatically learns and applies patterns for lumen direction detection, substituting traditional mechanical processing approaches with intelligent algorithms that achieve higher precision
2Reliability
If accurate lumen direction detection is achieved through machine learning, then the reliability of insertion guidance is improved, but the loss of time for model training and processing increases
Solution Approach 1:
The patent performs the computationally intensive machine learning model training in advance, before actual endoscopic procedures. The trained model is then deployed for rapid inference during procedures, separating the time-consuming training phase from the time-critical application phase, thus achieving both high reliability and efficient real-time processing
Solution Approach 2:
The patent creates a trained model that copies and generalizes the expertise of multiple annotated examples. Once trained on diverse data, the model can rapidly process new images without requiring manual annotation for each case, significantly reducing processing time while maintaining high accuracy through the learned patterns
3Measurement precision
If multiple detection methods are used to improve detection accuracy, then the measurement precision is improved, but the device complexity and ease of operation are worsened
Solution Approach 1:
The patent merges multiple detection approaches into a single integrated machine learning model. The model combines various image features and patterns that would otherwise require separate detection methods, unifying them into one system that automatically processes images and outputs direction information, thereby maintaining high precision while simplifying operation for medical professionals
Data Source
AI summary
An image processing device includes a processor, in which the processor acquires a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope, in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image, and outputs lumen direction information that is information indicating the lumen direction.