Conformal multi-material mesh generation from labelled medical volumes (Dec 2012)
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Transcript of Conformal multi-material mesh generation from labelled medical volumes (Dec 2012)
![Page 1: Conformal multi-material mesh generation from labelled medical volumes (Dec 2012)](https://reader034.fdocuments.net/reader034/viewer/2022042607/55a68df11a28abb27d8b47ad/html5/thumbnails/1.jpg)
1 Challenge the future
Conformal multi-material mesh generation from labelled medical volumes
![Page 2: Conformal multi-material mesh generation from labelled medical volumes (Dec 2012)](https://reader034.fdocuments.net/reader034/viewer/2022042607/55a68df11a28abb27d8b47ad/html5/thumbnails/2.jpg)
2 Challenge the future
Introduction
• Generation of volume meshes for FEA
• Particular use case: hip prostheses analysis
• Typical pipeline:
Segmentation from patient’s CT-scan (a) to labelled volume image (b). Volume Meshing (c) of the image and FEA for stress-strain results (d,[Dick2011]).
![Page 3: Conformal multi-material mesh generation from labelled medical volumes (Dec 2012)](https://reader034.fdocuments.net/reader034/viewer/2022042607/55a68df11a28abb27d8b47ad/html5/thumbnails/3.jpg)
3 Challenge the future
Introduction
• Mesh requirements:
• precise meshes
• segmentation-conform
• minimal mesh element number feature-adaptive
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4 Challenge the future
Related Work
Weighted Delaunay Tetrahedralization refinement [Boltcheva2009]
Dynamic Particle System Meshing [Meyer2007]
Multi-labelled volumes meshes with particle systems [Meyer2008]
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5 Challenge the future
Challenges
• long computation time • oversampling of edges and corners • no sharp-feature recreation ε-sample requirement
wrong topology, bad
reconstruction
too many samples
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6 Challenge the future
Contribution
• Application of Integer Medial Axis (IMA) as fast, discrete
medial axis scheme
• proposal of local surface triangulation scheme for volume
images
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7 Challenge the future
Integer Medial Axis - Analysis
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8 Challenge the future
Integer Medial Axis - Idea
BioMesh3D – Centres of
maximal spheres
IMA – shortest path in feature
transform
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9 Challenge the future
Integer Medial Axis – Results
Runtime
dataset BioMesh3D DeVIDE FE-Mesher
artificial 26 min 0.1 sec
Tooth 1h 41 min 1 sec
real femur 14h 11 min 2 sec
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10 Challenge the future
Integer Medial Axis – Results
Quality
Tooth # Tetra 142795 DeVIDE FE-Mesher
Max. Min. Avg. Variance # bad
Tetra
%
bad
Aspect
Ratio
119.69 1.01 1.94 1.00 9282 6.50
Radius
Ratio
105.43 1.00 1.69 0.83 6736 4.72
Volume 272.34 0.0 2.96 21.74 0 0.0
Tooth # Tetra 118110 Simpleware FE+
Max. Min. Avg. Variance # bad
Tetra
%
bad
Aspect
Ratio
44.59 1.02 1.54 0.16 816 0.69
Radius
Ratio
921.55 1.00 1.37 7.92 997 0.84
Volume 46.68 0.0 3.41 14.24 0 0.0
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14 Challenge the future
Integer Medial Axis – Results
Precision
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16 Challenge the future
Minimal Sample for accurate Meshing Concept
• ε-sampling:
• ensures topologic conformity
• applies to dense and sparse
samples
• Loss of sharp features
• only applies for 3D meshes
without additional information
• our idea:
• mesh surface locally
• take surface mesh to
generate volume mesh
0,, xxBESx
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17 Challenge the future
Minimal Sample for accurate Meshing Concept
1. Get TBN-Matrix per sample
vertex
2. Get Neighbourhood per
vertex
3. re-project points in
tangent plane
4. mesh via Local Delaunay
Triangulation tangent
plane neighbourhood
5. use established
connections in 3D
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18 Challenge the future
Minimal Sample for accurate Meshing Results
VTK CGAL – no constraint CGAL – Convex Hull constraint
formation of holes due unsuitable Neighbourhood determination
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19 Challenge the future
Conclusion and Future Work
• Improved runtime behaviour due to Medial Axis Transform
Algorithm change
• Local Triangulation in tangent space not ε-sample bound, but
dependent on Neighbourhood operation
• k-Nearest Neighbour not suitable for non-uniformal, sparse
samples
• In future: usage of natural neighbours for neighbourhood
determination