Automated 3D Cranial Image Orientation and Cropping
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Solution Overview
Problem
Existing cranial remodeling systems require manual operator intervention for data reorientation and cropping, leading to inefficiencies and inconsistencies in processing three-dimensional head shape data, which hinders the automation of cranial device production for deformity correction.
Innovation Solution
An automated system and method that orients and crops three-dimensional digital image representations of a subject's cranium independently of operator intervention, using a database of reference images to align and register new data meshes, and applies algorithms like Procrustes and Gaussian weighted centers to define axes and crop planes, ensuring consistent orientation and cropping for further processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual operator intervention is used for data reorientation and cropping, then flexibility in handling individual cases is improved, but processing efficiency and consistency deteriorate
Solution Approach 1:
The system performs self-service by automatically detecting anatomical landmarks (nasion, inion, ear positions) and computing orientation axes without human intervention. The automated cropping algorithm independently identifies and removes extraneous regions based on computed reference axes, eliminating the need for manual operator input while maintaining processing flexibility.
Solution Approach 2:
The system changes parameters by automatically adjusting orientation angles and cropping boundaries based on detected anatomical features. The reference axes (x-axis through nasion-inion, y-axis through ear positions, z-axis perpendicular to both) are dynamically computed for each subject, allowing adaptive processing that maintains flexibility while improving efficiency and consistency.
2Adaptability or versatility
If manual operator intervention is used for data reorientation and cropping, then handling of clinical adaptations is improved, but processing consistency and automation level deteriorate
Solution Approach 1:
The system handles clinical adaptations through self-service by automatically detecting anatomical landmarks and computing subject-specific orientation axes. The automated algorithm adapts to individual anatomical variations without manual intervention, ensuring consistent processing across all subjects while maintaining the ability to handle clinical adaptations.
Solution Approach 2:
The system replaces the mechanical manual operation with an automated computational system. The processor executes algorithms that detect anatomical landmarks, compute reference axes, and perform cropping automatically, substituting human manual operations with automated image processing and computational geometry operations that ensure consistency.
3Productivity
If automated processing is implemented, then production efficiency and consistency are improved, but ability to handle individual clinical adaptations deteriorates
Solution Approach 1:
The automated system maintains adaptability through self-service by automatically detecting anatomical landmarks and computing subject-specific orientation parameters. The system adapts to individual clinical cases by identifying unique anatomical features and adjusting processing parameters accordingly, all without manual intervention, thus maintaining both efficiency and adaptability.
Solution Approach 2:
The system maintains adaptability through parameter changes by dynamically computing orientation axes and cropping boundaries based on detected anatomical landmarks for each subject. The automated algorithm adjusts processing parameters (rotation angles, cropping regions) according to individual anatomical variations, ensuring both high productivity and clinical adaptability.
4Ease of operation
If trained operators manually reorient and crop data, then clinical judgment can be applied, but operator independence and automation level deteriorate
Solution Approach 1:
The system achieves operator independence through self-service by automatically performing all reorientation and cropping operations without human input. The automated detection of anatomical landmarks and computation of reference axes replaces manual operator actions, eliminating the need for trained operators while maintaining the ability to apply clinical judgment through algorithmic decision-making.
Solution Approach 2:
The system replaces manual operator operations with automated computational processes. The processor executes algorithms that detect anatomical features, compute orientation axes, and perform cropping automatically, substituting the mechanical action of manual manipulation with automated image processing that achieves operator independence.
Data Source
AI summary
A method for processing a three-dimensional image file captured directly from a live subject, the file including the cranium of the subject, comprises: providing a vertex point cloud for the three-dimensional image file; determining a median point for the vertex point cloud; determining a point on the cranium; and utilizing the median point and the cranium point to define a z-axis for the three-dimensional image file.