Automated 3D Channel Identification Using Homotopic Operations
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Solution Overview
Problem
Current methods for identifying myocardium conductive channels in 3D volumes are labor-intensive and lack automation, relying on visual inspection which is time-consuming and prone to errors, especially in the medical field where timely and accurate identification is crucial for treating arrhythmia.
Innovation Solution
A computer-implemented method that automatically identifies channels by processing 3D volume data to distinguish between different zones based on physical and functional properties, using homotopic operations to determine constrained channels within topological spaces, thereby reducing human error and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection methods are used to identify myocardium conductive channels, then human expertise can be applied to interpret complex patterns, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual visual inspection with an automated computer-implemented method that uses image processing algorithms and topological analysis to identify conductive channels. The system automatically processes 3D volumetric data, performs zone segmentation based on signal intensity, and applies homotopic operations to detect constrained channels, eliminating the need for labor-intensive manual review while maintaining identification accuracy.
Solution Approach 2:
The system enables self-service by allowing the computer-implemented method to autonomously perform the complete identification process without human intervention. The algorithm automatically segments zones, identifies candidate channels, performs homotopic operations, and produces results, making the process independent of manual labor while preserving the ability to accurately identify constrained channels.
2Adaptability or versatility
If manual identification methods are used, then flexibility in handling complex cases can be maintained, but the process lacks automation and consistency
Solution Approach 1:
The patent applies parameter changes by using multiple signal intensity thresholds to define different zones (first type, second type, third type) within the 3D volume. The system adjusts parameters such as signal intensity ranges and zone boundaries to adapt to different tissue characteristics, enabling automated identification while maintaining versatility in handling various cardiac conditions and anatomical variations.
Solution Approach 2:
The computer-implemented method achieves universality by providing a multi-functional system that can handle various types of conductive channel identification tasks. The same algorithmic framework processes 3D volumetric data, performs zone segmentation, identifies candidate channels, and applies homotopic operations across different cases, ensuring consistent automated processing while adapting to diverse medical scenarios.
3Productivity
If automated image processing is applied, then processing speed increases, but the complexity of the system increases
Solution Approach 1:
The patent applies segmentation by dividing the 3D volumetric data into distinct zones based on signal intensity thresholds. The system segments the volume into first type zones (low signal intensity), second type zones (intermediate signal intensity), and third type zones (high signal intensity), allowing the complex identification process to be broken down into manageable steps that can be processed automatically and efficiently.
Solution Approach 2:
The system handles complexity by working in three-dimensional space rather than two dimensions. The computer-implemented method processes 3D volumetric data, performing homotopic operations in 3D topological space to identify constrained channels. This dimensional approach allows the system to capture the spatial relationships and complex geometries of conductive channels more effectively, improving processing efficiency despite the increased data complexity.
Data Source
AI summary
The method comprises identifying, in a 3D volume, a zone of a first type (H), a zone of a second type (BZ) and a zone of a third type (C) and: —automatically identifying as a candidate channel (bz) a path running through the zone of a second type (BZ) and extending between two points of the zone of a first type (H); and—automatically performing, on a topological space (H_and_BZ_topo), homotopic operations between the candidate channel (bz) and paths (h) running only through the zone of a first type (H), and if the result of said homotopic operations is that the candidate channel (bz) is not homotopic to any path running only through the zone of a first type (H) identifying the candidate channel (bz) as a constrained channel. The computer program product implements the steps of the method of the invention.