Automated 3D Anatomical Model Generation from 2D Medical Images
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Solution Overview
Problem
Current methods for creating 3D printed patient-specific anatomical models from 2D medical images are complex, time-consuming, and require extensive technical knowledge, limiting their use in orthopaedic surgeries due to high costs and potential complications, and are not easily accessible to most surgeons.
Innovation Solution
A method and system for automatically generating 3D printable models from 2D medical images using segmentation techniques like threshold-based, decision tree, and neural network methods, integrated with a web application for easy data upload, real-time analysis, and 3D printing, with features like automatic material selection and smart contract validation on a Blockchain for secure and efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual segmentation techniques are used to create 3D printed anatomical models, then manufacturing precision is improved, but device complexity and time consumption increase
Solution Approach 1:
The patent segments the complex manual segmentation process into automated computational steps using machine learning algorithms. The system divides the anatomical structure identification into training phase and inference phase, where pre-trained models automatically segment anatomical features from medical images without requiring manual intervention, thus maintaining precision while reducing operational complexity
Solution Approach 2:
The patent replaces the mechanical manual segmentation process with an automated computational system. Instead of clinicians manually tracing and defining anatomical boundaries, the system uses trained neural networks and machine learning models to automatically identify and segment anatomical structures from medical images, substituting human manual operations with automated algorithms
2Manufacturing precision
If manual segmentation techniques are used to create 3D printed anatomical models, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by pre-training segmentation models on large datasets of medical images and anatomical annotations before actual use. The machine learning models are trained in advance to recognize anatomical structures, so when clinical images are processed, the segmentation occurs rapidly without requiring real-time manual intervention, thus reducing time loss while maintaining precision
Solution Approach 2:
The patent substitutes the time-consuming manual segmentation process with automated machine learning-based segmentation. The system uses pre-trained models that can rapidly process medical images and generate accurate 3D anatomical models automatically, replacing the slow manual process with fast computational processing
3Productivity
If automated segmentation techniques are used to create 3D printed anatomical models, then productivity is improved, but manufacturing precision may deteriorate
Solution Approach 1:
The patent uses preliminary action by extensively training the automated segmentation models on large, diverse datasets of medical images with expert-annotated ground truth before deployment. This pre-training ensures that the automated system learns accurate anatomical boundaries and structures, so when processing clinical images, it maintains high precision while delivering fast automated results
Solution Approach 2:
The patent implements feedback mechanisms where the automated segmentation results are validated and refined. The system uses feedback from training data and can incorporate validation steps to ensure accuracy, allowing automated processing to maintain manufacturing precision while improving productivity through automation
4Reliability
If 3D printing is implemented for patient-specific anatomical models, then reliability of surgical outcomes is improved, but device complexity and cost increase
Solution Approach 1:
The patent applies universality by creating a multi-functional integrated system that combines medical image processing, automated machine learning-based segmentation, 3D model generation, and 3D printing capabilities into a single platform. This universal system handles multiple functions (image analysis, model creation, and physical production) through one integrated workflow, reducing the need for separate complex systems while improving surgical outcome reliability
Data Source
AI summary
There is provided a method for generating a 3D physical model of a patient specific anatomic feature from 2D medical images. The 2D medical images are uploaded by an end-user via a Web Application and sent to a server. The server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique. The 3D printable model is 3D printed as a 3D physical model such that it represents a 1:1 scale of the patient specific anatomic feature. The method includes the step of automatically identifying the patient specific anatomic feature.