Anatomical Finite Element Model Generation via Segmentation
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Solution Overview
Problem
Current finite element modeling techniques for anatomical structures require multiple iterations and manual adjustments to achieve an optimal mesh, which can be time-consuming and prone to errors, especially due to the complexity of anatomical geometries and boundary conditions.
Innovation Solution
A system and method that utilize a segmentation model and association data to generate a finite element model by fitting a pre-determined anatomical region of interest with a specific mesh property, such as mesh resolution or element type, based on material type, mechanical boundary conditions, or anatomical characteristics, reducing the need for manual input and iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual assessment and multiple iterations are used to generate optimal mesh in FE models, then manufacturing precision of the mesh can be improved, but loss of time and productivity deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically identifying regions of interest and pre-determining optimal mesh properties before the actual mesh generation process. Association data is created in advance that links anatomical regions with their required mesh characteristics, eliminating the need for multiple iterative manual adjustments during mesh generation.
Solution Approach 2:
The system enables self-service by automatically assessing mesh requirements based on anatomical structure characteristics without requiring manual user intervention. The automated identification of regions of interest and their corresponding mesh properties allows the system to generate optimal meshes independently, reducing both time and potential user errors.
2Manufacturing precision
If fine granularity of sub-domains is used in regions of most interest, then manufacturing precision of the mesh is improved, but use of energy and computational power increase
Solution Approach 1:
The system applies local quality by automatically identifying specific regions of interest within the anatomical structure and assigning different mesh properties to different regions. Fine mesh resolution is applied only to identified regions of interest where high precision is needed, while coarser mesh is used in peripheral regions, optimizing the balance between accuracy and computational resources.
Solution Approach 2:
The system changes mesh parameters dynamically based on the identified anatomical regions. Association data stores and applies different mesh properties (such as element size, type, or density) to different regions of the anatomical structure, allowing fine granularity where needed and coarser granularity elsewhere, thus reducing overall computational power requirements.
3Manufacturing precision
If user manually adjusts mesh parameters to achieve optimal mesh, then manufacturing precision can be improved, but device complexity and ease of operation worsen
Solution Approach 1:
The system performs self-service by automatically identifying regions of interest and determining optimal mesh parameters without requiring manual user input or adjustment. The automated process eliminates the need for users to understand complex meshing parameters or perform iterative adjustments, significantly improving ease of operation while maintaining high mesh quality.
Solution Approach 2:
The system introduces an intermediary layer in the form of association data that automatically translates anatomical structure characteristics into appropriate mesh properties. This intermediary eliminates the need for direct user interaction with complex meshing parameters, simplifying the user interface while preserving manufacturing precision.
Data Source
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AI summary
A system and method is provided for generating a finite element (FE) model of an anatomical structure based on a fitted model (340) of the anatomical structure and association data. A segmentation model (310) may be provided for segmenting the anatomical structure. Association data may be obtained which associates a segmentation model part (315) of the segmentation model (310) with a mesh property, the segmentation model part (315) representing a pre-determined anatomical region of interest. The segmentation model may be applied to a medical image (320) of a subject, thereby obtaining a fitted model (340) providing a segmentation of the anatomical structure (330). The finite element model (350) may then be generated based on the fitted model (340) and the association data, said generating comprising meshing a finite element model part of the finite element model in accordance with the mesh property, the finite element model part corresponding with the pre-determined anatomical region of interest. Advantageously, this may result in an efficient generation of the FE model needing fewer manual iterations and/or alterations in the model or in the mesh.