3D Camera Pose Estimation Using 2D Laparoscopic Ridges
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Solution Overview
Problem
Current methods for registering 2D laparoscopic images with 3D models in laparoscopic procedures are impractical due to their slowness and inability to keep pace with changing 2D images during surgery.
Innovation Solution
A method and system for estimating 3D camera pose based on 2D features detected from 2D images, involving the generation of virtual 3D camera poses and virtual 2D images, and obtaining 2D feature/camera pose mapping models to facilitate accurate registration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feature point selection is used for registration, then registration accuracy can be achieved, but the process becomes slow and impractical for real-time surgery
Solution Approach 1:
The system automatically detects 2D features (ridges, contours) from laparoscopic images and performs registration without requiring manual feature point selection by surgeons or assistants. The automated feature detection and matching algorithms enable the system to self-perform the registration task, achieving both accuracy and real-time performance
Solution Approach 2:
The manual mechanical process of selecting feature points is replaced with automated image processing algorithms that detect 2D features, extract ridge information, and perform registration computationally. This substitution of manual operation with automated computational methods resolves the contradiction between accuracy and speed
2Reliability
If manual registration is performed, then initial alignment can be achieved, but it cannot keep pace with changing 2D images during surgery
Solution Approach 1:
The registration system transitions from a static manual process to a dynamic automated process that continuously adapts to changing 2D images. The system dynamically detects features in real-time and updates registration parameters as the surgical scene changes, maintaining both stability and real-time performance
Solution Approach 2:
The system performs preliminary automated feature detection and registration setup before surgery begins, and maintains readiness to process changing images throughout the procedure. This preliminary preparation and continuous operation enable the system to keep pace with dynamic surgical conditions
Data Source
AI summary
The present teaching is directed to estimating 3D camera pose based on 2D features detected from a 2D image. Virtual 3D camera poses are generated with respect to a 3D model for a target organ and associated anatomical structures. Virtual 2D images are created by projecting the 3D model from perspectives determined based on the virtual 3D camera poses. Each virtual 2D image includes 2D projected target organ and/or 2D structures of some 3D anatomical structures visible from a corresponding perspective. 2D feature/camera pose mapping models are then accordingly obtained based on 2D features extracted from the virtual 2D images and the corresponding virtual 3D camera poses, where the 2D features include a 2D ridge line projected from a 3D ridge on the target organ represented in the 3D model.