Endoscopic Image Processing System for Tension Detection
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Solution Overview
Problem
Existing methods for detecting appropriate tension during endoscopic surgery either require force sensors or involve costly machine learning training to classify force levels from images, and do not provide effective presentation methods for operators.
Innovation Solution
An image processing system that acquires time-series images from an endoscope, disposes an evaluation mesh with analysis points, deforms the mesh to track characteristic points, calculates deformation quantities, and presents information on mesh deformation to help operators determine pulling states and tension levels without direct force detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If force sensors are used to detect tension during endoscopic surgery, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces mechanical force sensors with an optical-based image processing system. By capturing images from multiple angles and analyzing deformation of the tissue surface using computer vision algorithms, the system determines tension levels without direct mechanical contact. This substitution eliminates the need for force sensors while maintaining measurement capability through optical field analysis.
Solution Approach 2:
The patent introduces an intermediary evaluation mesh as a virtual reference framework. This mesh is superimposed on the tissue surface in images and deforms along with the tissue, serving as a mediator to quantify surface deformation. By tracking the deformation of this virtual mesh rather than directly measuring force, the system indirectly determines tension levels without requiring force sensors.
2Measurement precision
If machine learning is used to classify force levels from images, then measurement precision is improved, but device complexity and training cost increase
Solution Approach 1:
The patent replaces complex machine learning-based force classification with a physics-based deformation analysis approach. By using the evaluation mesh to track and quantify surface deformation patterns, and comparing these patterns against predetermined tension criteria, the system achieves force level classification through geometric and mechanical principles rather than data-driven machine learning models.
Solution Approach 2:
The patent performs preliminary action by pre-defining evaluation meshes and establishing predetermined tension determination criteria before actual measurement. The evaluation mesh structure and the relationship between deformation patterns and tension levels are prepared in advance, allowing real-time tension assessment without requiring complex runtime machine learning inference or training.
3Measurement precision
If direct force detection is implemented, then measurement precision is improved, but ease of operation deteriorates due to lack of visual feedback
Solution Approach 1:
The patent employs visual feedback by displaying the deformation state of the evaluation mesh, potentially using color coding or other visual indicators to represent different tension levels. This allows operators to visually assess tissue tension directly from the image display, providing intuitive feedback without requiring complex instrument readings or manual interpretation of force sensor data.
Solution Approach 2:
The evaluation mesh serves as a visual intermediary that translates invisible force into visible deformation patterns. By displaying how the mesh deforms on the tissue surface, the system provides operators with direct visual information about tension levels, making the invisible mechanical force observable and easier to interpret during surgery.
Data Source
AI summary
An image processing system includes one or more processors comprising hardware configured to sequentially acquire time-series images captured by an endoscope, a dispose an evaluation mesh including a plurality of analysis points in a freely-selected timing image out of the time-series images, deform the evaluation mesh in each image of the time-series images so that each analysis point in each image of the time-series images tracks a characteristic point of an object located on each analysis point in the freely-selected timing image in which the evaluation mesh is disposed, calculate a deformation quantity of each cell of the evaluation mesh based on magnitude and a direction of a movement quantity of each analysis point in each image, and present information regarding deformation of the evaluation mesh based on the calculated deformation quantity.