Automated 3D Orbital Fracture Reconstruction Using CT Image Segmentation
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Solution Overview
Problem
Current methods for surgical reconstruction of orbital fractures are hindered by poor contrast in medical images of orbital bones, making manual 3D model reconstruction time-consuming and labor-intensive, especially for the medial and inferior walls which are commonly fractured.
Innovation Solution
An image processing system is developed to automatically detect orbital fractures in CT scans, enhance contrast, segment regions of interest, and generate 3D models for fabricating patient-specific orbital implants and retractors, using machine learning and algorithms like marching cubes for efficient 3D reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation and reconstruction methods are used, then the 3D model can be accurately reconstructed, but the process is extremely time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical segmentation processes with automated image processing algorithms and machine learning models. The system uses computer vision techniques to automatically detect orbital bones, segment fracture regions, and reconstruct 3D models, eliminating the need for manual point-by-point tracing while maintaining high accuracy.
Solution Approach 2:
The system enables the imaging data to self-segment and self-reconstruct through automated algorithms. The machine learning model automatically identifies anatomical structures and fracture patterns without requiring continuous human intervention, allowing the reconstruction process to serve itself rather than relying on manual operation.
2Measurement precision
If manual segmentation is performed to achieve accurate fracture detection, then the reconstruction quality is maintained, but the process becomes extremely labor-intensive
Solution Approach 1:
The patent replaces manual detection and segmentation operations with automated image processing systems. Machine learning algorithms automatically identify fracture patterns, while computer vision techniques detect orbital bone structures, eliminating the need for manual analysis and significantly reducing labor intensity while maintaining high detection accuracy.
Solution Approach 2:
The system introduces intermediate processing steps including image enhancement, edge detection, and feature extraction algorithms that bridge the gap between raw imaging data and final 3D reconstruction. These intermediary automated processes handle the complex detection tasks, making the overall system easier to operate while maintaining precision.
3Loss of information
If traditional imaging methods are used, then the orbital bones can be visualized, but the signal contrast between orbital bones and adjacent tissues is poor
Solution Approach 1:
The patent applies image processing techniques that modify parameters such as contrast enhancement, histogram equalization, and intensity normalization to improve the visibility of orbital bone structures. These parameter changes amplify the signal difference between bones and soft tissues, making fracture detection easier without requiring changes to the imaging hardware.
Solution Approach 2:
The system introduces intermediate image processing steps including filtering, edge enhancement, and multi-scale analysis that act as mediators between the raw low-contrast images and the final high-visibility reconstruction. These intermediary processes improve bone structure visibility by enhancing edges and suppressing soft tissue signals.
Data Source
AI summary
Techniques for fabrication of implant material for the reconstruction of fractured eye orbit may include using an image processing system to analyze a set of two-dimensional images representing a three-dimensional scan of a skull of a patient, automatically detect an orbital fracture in the skull based on the set of two-dimensional images, and identify which/both of the two eye orbits containing any orbital fracture. The techniques may further include, for each of the two-dimensional images in which the orbital fracture is detected, determining a region of interest, and extracting the region of interest. The techniques may further include generating a three-dimensional reconstruction model for the fractured eye orbit, and outputting model data for generating an implant mold for the fractured eye orbit.