Neural Network Camera Tracking for Surgical Instrument Accuracy
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Solution Overview
Problem
Existing camera tracking systems for computer-assisted surgery navigation face inaccuracies due to deformed instruments, reference elements, and optical marker issues, which affect the fidelity of instrument tracking during surgical procedures.
Innovation Solution
A camera tracking system utilizing neural networks to predict feature coordinate locations and accuracy cameras for verification, along with reference markers and calibration patterns, to enhance the accuracy of instrument tracking and navigation during surgery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mechanical verification divots and manual positioning methods are used, then instrument accuracy can be verified, but the surgical workflow becomes complex and time-consuming
Solution Approach 1:
The patent replaces mechanical verification divots and manual positioning methods with an automated optical measurement system. Tracking cameras capture images of the instrument tip and reference arrays, while a processor automatically calculates the instrument tip location and compares it with the computer model prediction, eliminating the need for physical verification features and manual checking procedures.
Solution Approach 2:
The system performs self-verification by automatically comparing the measured instrument position from tracking cameras with the predicted position from the computer model. The processor autonomously determines whether the instrument is properly positioned and generates verification results without requiring manual intervention or external verification tools.
2Ease of manufacture
If traditional optical markers are used for tracking, then the system is simpler to implement, but manufacturing defects and marker inaccuracies reduce tracking precision
Solution Approach 1:
The patent implements a feedback mechanism where the system captures images of optical markers with tracking cameras, processes the marker locations to determine instrument position, compares this with the computer model prediction, and provides verification feedback. This closed-loop system compensates for marker inaccuracies by continuously monitoring and adjusting the verification process.
Solution Approach 2:
The system uses a digital copy of the instrument model in computer space to represent the physical instrument. The computer model contains the expected positions of optical markers and instrument features, allowing the system to compare actual marker locations with predicted locations to verify instrument positioning accuracy without requiring perfectly manufactured physical markers.
3Device complexity
If manual instrument verification is performed, then the system requires fewer automated components, but the surgical procedure time increases
Solution Approach 1:
The patent performs verification actions automatically during the surgical procedure using tracking cameras and image processing. The system captures images of the instrument tip and reference arrays, processes these images to determine positions, compares with the computer model, and provides real-time verification feedback, eliminating the need for separate manual verification steps and improving surgical efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system improves the accuracy of instrument tracking by eliminating the need for mechanical verification divots and providing real-time, automated measurement, ensuring precise navigation and reducing errors in surgical procedures.
Implementation Method 1
Camera tracking systems for computer assisted surgery navigation typically use a set of tracking cameras to track pose of a reference array on the surgical instrument
Implementation Method 2
The operations process a region of interest in the set of the images identified based on the measured coordinate locations through a neural network configured to output a prediction of coordinate locations of the feature in the set of the images
Implementation Method 3
The accuracy cameras are connected to a reference array of markers through a rigid fixture... The operations identify an actual calibration pattern in the images from the accuracy cameras
Data Source
AI summary
A camera tracking system for computer assisted navigation during surgery. Operations identify locations of markers of a reference array in images obtained from tracking cameras imaging a real device. Operations determine measured coordinate locations of a feature of a real device in the images based on the identified locations of the markers and based on a relative location relationship between the markers and the feature. Operations process a region of interest in the images identified based on the measured coordinate locations through a neural network configured to output a prediction of coordinate locations of the feature in the images. The neural network has been trained based on training images containing the feature of a computer model rendered at known coordinate locations. Operations track pose of the feature of the real device in 3D space based on the prediction of coordinate locations of the feature of the real device in the images.