Colonoscopy Polyp Tracking GUI With 3D Coverage Feedback
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Solution Overview
Problem
Existing colonoscopy procedures face challenges in accurately detecting and removing polyps due to operator fatigue, inattention, and limitations in visualization, leading to missed polyps and potential cancer risks.
Innovation Solution
A system and method for dynamically tracking 3D movement of an endoscopic camera within the colon, using neural networks to reconstruct 2D images into 3D, and generating a GUI that provides real-time feedback on polyp locations, coverage, and camera movement directions to ensure comprehensive imaging and polyp removal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time 3D reconstruction and tracking is implemented, then polyp detection accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The system pre-processes endoscopic images by segmenting the colon wall and lumen, and pre-computes 3D coordinates for pixels before polyp detection is needed. This preliminary 3D reconstruction enables faster real-time tracking and detection during the actual procedure, reducing computational burden during critical detection phases.
Solution Approach 2:
The patent introduces an intermediary coordinate system that maps 2D image pixels to 3D colon wall coordinates. This intermediary representation serves as a bridge between the endoscopic camera views and the actual polyp locations, enabling accurate detection without requiring complex direct 3D reconstruction during real-time operation.
2Reliability
If comprehensive coverage tracking is implemented, then polyp detection completeness is improved, but system complexity increases
Solution Approach 1:
The system continuously tracks the 3D position of the endoscopic camera and provides real-time feedback on which portions of the colon have been imaged and which remain uncovered. This feedback mechanism guides the operator to ensure comprehensive coverage and prevents missed polyps, while the tracking complexity is managed through efficient coordinate transformation algorithms.
3Measurement precision
If 3D reconstruction neural network is used, then visualization accuracy is improved, but processing speed decreases
Solution Approach 1:
The neural network performs 3D reconstruction on pre-captured endoscopic images before the procedure begins or during setup phases. This preliminary reconstruction creates accurate 3D models and coordinate mappings that can be reused during the actual colonoscopy, avoiding the need for real-time neural network processing during the procedure and thus maintaining both accuracy and speed.
Data Source
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AI summary
There is provided a method of generating instructions for presenting a graphical user interface (GUI) for dynamically tracking at least one polyp in a plurality of endoscopic images of a colon of a patient, comprising: iterating for the plurality of endoscopic images: tracking a location of a region depicting at least one polyp within the respective endoscopic image relative to at least one previous endoscopic image, when the location of the region is external to the respective endoscopic image: computing a vector from within the respective endoscopic image to the location of the region external to the respective endoscopic image, creating an augmented endoscopic image by augmenting the respective endoscopic image with an indication of the vector, and generating instructions for presenting the augmented endoscopic image within the GUI.