Catheter Image Classification for Intracardiac Procedures
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Solution Overview
Problem
It is challenging for users to quickly and accurately understand images acquired by an image acquisition catheter, particularly in complex structures like the intracardiac region, due to the difficulty in distinguishing between biological and non-biological tissue regions.
Innovation Solution
An information processing device and method that utilize a trained model to classify catheter images into biological tissue regions, non-biological tissue regions, and medical instrument regions, using machine learning techniques such as convolutional neural networks to generate classification data and assist in understanding the image acquisition catheter's output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a trained model is used to classify catheter images into biological and non-biological tissue regions, then the user's ability to understand and interpret images is improved, but the device complexity increases due to the need for machine learning models and training data processing
Solution Approach 1:
The system performs preliminary classification of catheter images into biological tissue regions, non-biological tissue regions, and medical instrument regions using a trained model before presenting the processed information to the user. This advance processing reduces the cognitive load during actual medical procedures while the complexity is managed offline during model training phases
Solution Approach 2:
A classification layer acts as an intermediary between the raw catheter image data and the user interface. The trained model serves as a mediator that automatically segments and labels different tissue types, translating complex image data into interpretable regional classifications without requiring direct user analysis of raw images
2Productivity
If classification data is generated using machine learning techniques, then the speed of image analysis is improved, but the loss of time occurs during the model training phase
Solution Approach 1:
The classification model is trained in advance using historical catheter image data and classification results. This preliminary training phase separates the time-consuming learning process from the actual clinical use, enabling rapid real-time classification during medical procedures without sacrificing analysis speed
Solution Approach 2:
The system employs dynamic learning where the model can be retrained or fine-tuned with new data as it becomes available, allowing the system to adapt and improve over time. The training process is made flexible and incremental rather than requiring complete retraining, reducing the time loss for updates
Data Source
AI summary
An information processing device that includes: an image acquisition unit that acquires a catheter image obtained by an image acquisition catheter inserted into a first cavity; and a first classification data output unit configured to input the acquired catheter image to a first classification trained model that, upon receiving input of the catheter image, outputs first classification data in which a non-biological tissue region including a first inner cavity region that is inside the first cavity and a second inner cavity region that is inside a second cavity where the image acquisition catheter is not inserted and a biological tissue region are classified as different regions, and outputs the first classification data, in which the first classification trained model is generated using first training data that indicates at least the non-biological tissue region including the first inner cavity region and the second inner cavity region and the biological tissue region.