3D Shape Data Generation Using Iterative Vertex Transformation
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Solution Overview
Problem
Existing techniques for generating 3-dimensional shape data of organs, particularly in medical simulators, face challenges due to the complexity of organ shapes and the limitations of tomographic imaging, often resulting in low accuracy when sufficient images are not obtained.
Innovation Solution
A method involving the transformation of a reference shape by specifying and transforming vertices based on conditions related to the target shape's boundaries, using techniques like Thin Plate Spline Warp and iterative landmark adjustments, to accurately generate 3D shape data even with insufficient tomographic images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional transformation methods are used to generate 3D shape data from tomographic images, then the process can be completed with available images, but the accuracy of the generated shape data becomes low when sufficient images are not obtained
Solution Approach 1:
The patent introduces a reference shape model as an intermediary between the limited tomographic images and the target 3D shape data. This reference model serves as a mediator that provides additional geometric information to compensate for insufficient imaging data, enabling accurate shape reconstruction even when the number of tomographic images is limited
Solution Approach 2:
The patent performs preliminary alignment and transformation of a reference shape model before final shape generation. By pre-positioning and pre-transforming the reference model based on available landmarks and boundary information, the system prepares an accurate initial 3D shape that can be refined with limited imaging data
2Ease of manufacture
If landmark-based transformation methods are used, then transformation can be executed with defined parameters, but unnatural shapes form unless source landmarks and target landmarks are set properly
Solution Approach 1:
The patent implements an iterative feedback mechanism where the transformed shape is continuously evaluated against the target boundary information. The transformation parameters are adjusted based on feedback from how well the transformed reference shape matches the actual organ boundaries, ensuring natural shape formation while maintaining ease of execution
Solution Approach 2:
The patent dynamically adjusts transformation parameters including scaling factors, rotation angles, and landmark positions based on the matching quality between the transformed reference shape and target boundary. These parameter changes enable the system to achieve natural shape formation automatically without requiring manual perfection of landmark placement
Data Source
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AI summary
A shape data generation method includes: generating a target shape of transformation from plural tomographic images of an object; specifying, from among plural vertices of a first shape that is a reference shape of the object, plural first vertices, each first vertex of which satisfies a condition that a normal line of the first vertex passes through a point that is located on the target shape and is located on a boundary of the object in any one of the plural tomographic images; identifying, for each of the plural first vertices, a second vertex that internally divides a segment between the first vertex and the point; transforming the first shape so as to put each of the plural first vertices on a corresponding second vertex; setting a shape after the transforming to the first shape; and executing the first specifying and the subsequent processings a predetermined number of times.