| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Dahl, Vedrana Andersen
Technical University of Denmark
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 2023Dataset for scanning electron microscopy based local fiber volume fraction analysis of non-crimp fabric glass fiber reinforced compositescitations
- 2023Elucidating the Bulk Morphology of Cellulose-Based Conducting Aerogels with X-Ray Microtomography
- 2023Elucidating the Bulk Morphology of Cellulose-Based Conducting Aerogels with X-Ray Microtomography
- 2022SparseMeshCNN with Self-Attention for Segmentation of Large Meshescitations
- 2021Quantifying effects of manufacturing methods on fiber orientation in unidirectional composites using structure tensor analysiscitations
- 2020Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensorcitations
- 2019Process characterization for molding of paper bottles using computed tomography and structure tensor analysis
- 2019Fiber segmentation from 3D X-ray computed tomography of composites with continuous textured glass fibre yarns
- 2019Structural Characterization of Membrane-Electrode-Assemblies in High Temperature Polymer Electrolyte Membrane Fuel Cellscitations
- 2014Surface Detection using Round Cutcitations
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article
SparseMeshCNN with Self-Attention for Segmentation of Large Meshes
Abstract
In many clinical applications, 3D mesh models of human anatomies are important tools for visualization, diagnosis, and treatment planning. Such 3D mesh models often have a high number of vertices to capture the complex shape, and processing these large meshes on readily available graphic cards can be a challenging task. To accommodate this, we present a sparse version ofMeshCNN called SparseMeshCNN, which can process meshes with more than 60 000 edges. We further show that adding non-local attention in the network can mitigate the small receptive field and improve the results. The developed methodology was applied to separate the Left Atrial Appendage (LAA) from the Left Atrium (LA) on 3D mesh models constructed from medicalimages, but the method is general and can be put to use in any application within mesh classification or segmentation where memory can be a concern.