Medical Instrument Tip Pose Estimation from 3D Point Clouds
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Solution Overview
Problem
Existing tip pose estimation techniques for flexible elongated medical instruments in medical procedures suffer from inaccuracies due to variations in shape and curvature, imaging artifacts, and manual steps, which can introduce errors.
Innovation Solution
An automated process using machine learning to estimate the tip pose in 3D space from externally captured CT images, such as CBCT, providing accurate alignment with targets during procedures like robotically assisted bronchoscopy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pose estimation techniques are used, then the process can be performed with simple equipment, but measurement precision deteriorates due to manual errors and inaccuracies
Solution Approach 1:
The patent replaces manual mechanical measurement methods with an automated machine learning-based pose estimation system. The system uses a processing system that receives image data, generates a point cloud representation of the instrument tip, and determines pose parameters through automated computation, eliminating manual measurement errors while maintaining computational resource requirements within acceptable limits.
Solution Approach 2:
The patent creates a digital copy (point cloud) of the instrument tip from image data to enable automated pose analysis. This point cloud representation serves as a simplified digital model that can be processed by machine learning algorithms to determine pose without requiring direct physical measurement or complex hardware.
2Measurement precision
If automated machine learning pose estimation is implemented, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the pose estimation process into distinct functional modules: image data reception, point cloud generation, and pose determination. This segmentation allows each component to be optimized independently and reduces overall system complexity by dividing the complex task into manageable steps that can be implemented with standard processing hardware.
Solution Approach 2:
The patent transforms the pose estimation problem by changing parameters from direct coordinate measurement to point cloud representation. This parameter transformation enables the use of machine learning models that can handle the point cloud data and output pose parameters, improving accuracy while keeping the processing requirements manageable through efficient algorithm design.
3Reliability
If existing pose estimation techniques are used, then device complexity remains low, but reliability deteriorates due to inaccuracies from shape variations and imaging artifacts
Solution Approach 1:
The patent incorporates feedback mechanisms where the system processes image data, generates point clouds, determines pose parameters, and can iteratively refine results based on the known geometry of the instrument. This feedback loop enables the system to correct errors introduced by shape variations and imaging artifacts, significantly improving reliability without requiring overly complex hardware.
Solution Approach 2:
The patent introduces a point cloud representation as an intermediary between the raw image data and the final pose determination. This intermediary layer processes and cleans the data, filtering out artifacts and normalizing variations in instrument geometry, thereby improving reliability while maintaining reasonable system complexity through efficient data processing.
Data Source
AI summary
This disclosure provides methods, devices, and systems for pose estimation. The present implementations more specifically relate to techniques for determining the pose of a medical instrument within an anatomy. In some aspects, a controller for a medical system may receive image data representing a three-dimensional (3D) model of an anatomy having an instrument disposed therein. The controller generates a point cloud associated with a distal end of the instrument based on the image data and determines a pose of the distal end of the instrument based at least in part on the point cloud and a known geometry of the distal end of the instrument.