Automated 3D Respiratory Tract Modeling with Endpoint Capping
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Solution Overview
Problem
Conventional methods for creating custom 3D models of the human respiratory tract are time-consuming, expensive, and prone to error, requiring substantial human input and being poorly suited for generating accurate models for drug dosing and environmental health analysis.
Innovation Solution
A method and system for automatically generating custom 3D models of the human respiratory tract from images such as CT scans, using computer-implemented algorithms to identify and cap endpoints, thereby producing a sealed 3D volume suitable for computational fluid and particle dynamics simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to create custom 3D models of the human respiratory tract, then human expertise and control are maintained, but the process is time-consuming, expensive, and prone to error
Solution Approach 1:
The patent replaces manual mechanical 3D modeling processes with automated computer-implemented algorithms that process medical images (CT scans, MRIs) to generate 3D models. The system automatically segments respiratory structures, reconstructs 3D geometries, and caps endpoints without requiring skilled modelers, thereby reducing time and cost while maintaining accuracy through algorithmic precision
Solution Approach 2:
The system enables self-service 3D model generation where the input medical images automatically trigger the complete modeling pipeline. The algorithms autonomously identify respiratory tract structures, generate 3D representations, and perform endpoint capping without human intervention, allowing users to obtain accurate models simply by providing imaging data
2Manufacturing precision
If manual 3D modeling is performed, then customization and precision can be achieved, but the process requires substantial human input and is expensive
Solution Approach 1:
The patent segments the complex respiratory tract into distinct anatomical structures (trachea, bronchi, bronchioles, alveoli) using automated image segmentation algorithms. Each structure is independently identified and modeled, allowing precise reconstruction of complex geometries while simplifying the overall process into manageable computational steps that automatically handle the complexity
Solution Approach 2:
The system introduces computer-implemented algorithms as intermediaries between the input medical images and the final 3D models. These algorithms act as mediators that automatically translate imaging data into accurate 3D representations, eliminating the need for human modelers to directly interpret and recreate complex anatomical structures while maintaining high precision
3Productivity
If automated algorithms are used to generate 3D models, then time and cost are reduced, but the ability to handle complex anatomical variations may be compromised
Solution Approach 1:
The patent implements dynamic algorithms that adapt to varying anatomical structures in real-time. The image segmentation and 3D reconstruction processes automatically adjust to accommodate individual variations in respiratory tract geometry, allowing the system to generate accurate models for diverse anatomical configurations without requiring manual customization for each case
Solution Approach 2:
The system changes processing parameters dynamically based on the input imaging data. The algorithms adjust segmentation thresholds, reconstruction parameters, and model resolution adaptively to match the specific anatomical features detected in each patient's CT scan or MRI, enabling high productivity while maintaining versatility across different anatomical variations
Data Source
AI summary
A method for modeling a human respiratory tract. The method includes receiving a number of images, wherein each of the number of images include at least a portion of a human respiratory tract, generating, based on the number of images, a 3D model of at least a portion of the human respiratory tract, wherein the 3D model includes an uncapped endpoint of the human respiratory tract, and automatically capping the endpoint within the 3D model by identifying, using a computer-implemented algorithm, the endpoint of the human respiratory tract within the 3D model, generating a 3D model of a shape, aligning the 3D model of the shape with the endpoint by positioning a center of the 3D model of the shape at a centroid corresponding to the endpoint, and merging the 3D model of the shape with the 3D model.