Virtual Colonoscopy Polyp Visibility Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In virtual colonoscopy, polyps located behind haustral folds or out of the limited field-of-view may be obscured, making them difficult for physicians to detect due to visual distortion and obstruction by anatomical features.
Innovation Solution
A method that preprocesses volumetric imaging data to identify polyp visibility by using distance masks, histogram analysis, and ray-casting operations to determine if polyps are within the direct line of sight, and if obstructed, flags them with markers for attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a limited field-of-view is used in virtual colonoscopy, then visual distortion is reduced, but polyps located behind haustral folds or out of view become obscured and difficult to detect
Solution Approach 1:
The system segments polyps from the surrounding anatomical structures by computing distance masks that separate polyp voxels from haustral folds and other tissues. This segmentation allows the system to identify and flag polyps that would otherwise be obscured by anatomical features, resolving the contradiction between maintaining visual accuracy and detecting all polyps.
Solution Approach 2:
The system transitions from 2D image analysis to 3D volumetric analysis by processing volumetric imaging data and computing distance masks in three dimensions. This dimensional change enables the system to detect polyps located behind haustral folds by analyzing spatial relationships in the volumetric data, thereby improving polyp detection completeness without compromising visual accuracy.
2Measurement precision
If volumetric data is processed to identify obscured polyps, then polyp detection accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary processing by pre-computing distance masks and identifying potential polyp locations before the actual diagnostic review. This preliminary action reduces the computational burden during real-time or near-real-time analysis, as the complex volumetric processing has already been completed in advance, thereby reducing processing complexity while maintaining high identification accuracy.
Solution Approach 2:
The system introduces distance masks as an intermediary data structure that simplifies the complex task of polyp identification. Instead of directly analyzing raw volumetric data to identify obscured polyps, the system first computes distance masks that encode spatial relationships and visibility information, making subsequent polyp detection more computationally efficient while maintaining high accuracy.
3Loss of information
If all polyps are marked regardless of visibility, then completeness of detection is improved, but visual clutter increases and reduces ease of interpretation
Solution Approach 1:
The system applies different marking strategies based on the local context of each polyp. Visible polyps are marked with one type of indicator, while obscured polyps are marked with a different type of indicator that conveys their hidden status. This local differentiation allows physicians to quickly distinguish between visible and obscured polyps, reducing visual clutter while maintaining detection completeness.
Solution Approach 2:
The system uses different visual indicators or color coding to distinguish between visible and obscured polyps. This visual differentiation helps physicians quickly understand the status of each marked location without having to interpret complex markings, thereby improving ease of interpretation while maintaining complete detection information.
Data Source
AI summary
A method of operating on volumetric imaging data representing organ anatomy includes determining whether a polyp location within a specified range from a viewing point is visible, or hidden by an anatomical feature, and marking the polyp as visible or hidden with a viewable indicator.