| 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, Anders Bjorholm
Technical University of Denmark
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (18/18 displayed)
- 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
- 2017Individual fibre segmentation from 3D X-ray computed tomography for characterising the fibre orientation in unidirectional composite materialscitations
- 2017Graphite nodules in fatigue-tested cast iron characterized in 2D and 3Dcitations
- 2015Dictionary Based Segmentation in Volumescitations
- 2015Characterization of boundary roughness of two cube grains in partly recrystallized coppercitations
- 2015Boundary Fractal Analysis of Two Cube-oriented Grains in Partly Recrystallized Coppercitations
- 2014Surface Detection using Round Cutcitations
- 2014Pattern recognition approach to quantify the atomic structure of graphenecitations
- 2014Structure Identification in High-Resolution Transmission Electron Microscopic Imagescitations
- 2014Characterization of graphite nodules in thick-walled ductile cast iron
- 2014Quantification Tools for Analyzing Tomograms of Energy Materials
- 2013Automated Structure Detection in HRTEM Images: An Example with Graphene
- 2012Large scale tracking of stem cells using sparse coding and coupled graphs
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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.