Catheter Instrument Orientation Detection Using DCNN Image Feedback
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Solution Overview
Problem
Existing minimally invasive medical techniques face challenges in accurately controlling the position and orientation of elongate devices, such as flexible catheters, due to the complexity of managing multiple degrees of freedom during insertion and steering, which can affect the precision and efficacy of procedures.
Innovation Solution
A method and system utilizing a deep convolutional neural network (DCNN) to analyze images captured during the installation of a tool within a catheter, applying perturbations to training images, and determining the tool's configuration based on analyzed images, enabling precise control and alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If minimally invasive techniques are used to reduce tissue damage, then patient recovery time and discomfort are reduced, but the complexity of managing multiple degrees of freedom during instrument insertion and steering increases
Solution Approach 1:
The patent implements a feedback mechanism where images captured during tool insertion are processed by a DCNN to determine tool configuration, and this information is fed back to the control system to automatically adjust catheter steering, reducing the manual control burden while maintaining precision
Solution Approach 2:
The patent replaces manual mechanical control of multiple degrees of freedom with an automated system that uses image processing and neural networks to determine tool configuration, substituting complex mechanical steering control with computational analysis and automated actuation
2Device complexity
If manual control of elongate device steering is used, then device complexity is reduced, but measurement precision of tool position and orientation deteriorates
Solution Approach 1:
The patent replaces manual mechanical steering control with an automated image-based detection system that uses DCNN to analyze captured images and precisely determine tool configuration, eliminating the trade-off between control simplicity and measurement precision
Solution Approach 2:
The patent introduces an intermediary image processing system with DCNN that mediates between the physical tool state and the control system, automatically extracting precise position and orientation information from captured images without requiring complex mechanical sensors
3Measurement precision
If real-time image analysis with DCNN is implemented, then measurement precision of tool configuration is improved, but computing time and processing resources increase
Solution Approach 1:
The patent performs preliminary action by pre-training the DCNN model using replicated and perturbed images before actual use, so that during real-time operation the model can quickly infer tool configuration without requiring complex computations, reducing processing time while maintaining precision
Data Source
AI summary
A method for determining a position of a tool being received by a catheter, the method including capturing first images with the tool as the tool is being installed in the catheter. The method also includes generating training images for a deep convolutional neural network (DCNN) by replicating the first images and applying perturbations to the replicated first images. The method also includes training the DCNN by inputting the training images into the DCNN. The method also includes capturing second images with the tool as the tool is being installed in the catheter. The method also includes inputting the second images into the DCNN. The method also includes analyzing the second images with the trained DCNN. The method further includes determining a configuration of the tool based on the analyzed second images.