Endoscope System Cross Section Error Detection
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Solution Overview
Problem
Existing endoscope systems often inaccurately estimate the shape of organs due to errors in position estimation, leading to incorrect rendering of plane or curved surfaces as lumen shapes, which reduces the reliability of three-dimensional shape estimation.
Innovation Solution
An endoscope system that includes an endoscope for image acquisition and a processor to estimate relative three-dimensional positions and shapes based on image data, generates cross sections along the endoscope's trajectory, and determines erroneous estimation portions using shape characteristics of these cross sections to improve the accuracy of three-dimensional shape estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If three-dimensional shape estimation is performed based on endoscopic images and position information, then spatial understanding of organ structures is improved, but estimation accuracy deteriorates due to errors in position estimation causing plane surfaces to be misidentified as lumen shapes
Solution Approach 1:
The patent segments the three-dimensional shape estimation process into multiple verification stages: initial shape estimation from point cloud data, cross-sectional shape extraction, and erroneous portion determination by comparing cross-sectional characteristics. This multi-stage segmentation allows identification and correction of estimation errors, particularly in distinguishing plane surfaces from lumen structures.
Solution Approach 2:
The patent introduces cross-sectional shape information as an intermediary verification layer between raw position estimation and final three-dimensional shape determination. By extracting and analyzing cross-sectional characteristics (such as circularity, area, and perimeter ratios), the system mediates the validation process to detect erroneous estimations caused by position errors.
2Reliability
If cross section analysis is performed to identify erroneous portions, then shape estimation reliability is improved, but processing complexity increases
Solution Approach 1:
The patent applies local quality analysis by examining cross-sectional characteristics at specific locations along the endoscope trajectory rather than analyzing the entire three-dimensional shape globally. By focusing on local cross-sectional properties (circularity, area, perimeter) at each measurement point, the system efficiently identifies erroneous portions without requiring complex global reprocessing.
Solution Approach 2:
The patent transforms the verification problem from three-dimensional spatial analysis to two-dimensional cross-sectional parameter analysis. By changing the analysis parameters from volumetric shape characteristics to cross-sectional metrics (circularity, area, perimeter ratios), the system simplifies error detection while maintaining reliability.
Data Source
AI summary
An endoscope system includes an endoscope that acquires an image of a subject and a processor. The processor acquires position and posture information of the endoscope, estimates a three-dimensional shape of the subject based on the image and the position and posture information, generates, based on the position information, a cross section crossing a trajectory on which the endoscope passed, and determines an erroneous estimation portion based on a shape characteristic of the three-dimensional shape on the cross section.