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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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Ryckelynck, David
Mines Paris - PSL
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (9/9 displayed)
- 2021A Modular U-Net for Automated Segmentation of X-Ray Tomography Images in Composite Materialscitations
- 2021A modular U-Net for automated segmentation of X-ray tomography images in composite materialscitations
- 2019Crystal plasticity modeling of the cyclic behavior of polycrystalline aggregates under non-symmetric uniaxial loading: Global and local analysescitations
- 2016Hyper-reduction framework for model calibration in plasticity-induced fatiguecitations
- 2015Modelling and prediction of deformation during sintering of a metal foam based SOFC (EVOLVE)
- 2014Architectured bimetallic laminates by roll bonding : bonding mechanisms and applicationscitations
- 2012Bimodal Beremin-type model for brittle fracture of inhomogeneous ferritic steels: Theory and applicationscitations
- 2012Beremin model: Methodology and application to the prediction of the Euro toughness data setcitations
- 2010Anisotropic constitutive model and FE simulation of the sintering process of slip cast traditional porcelaincitations
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article
A modular U-Net for automated segmentation of X-ray tomography images in composite materials
Abstract
X-ray Computed Tomography (XCT) techniques have evolved to a point that high-resolution data can be acquired so fast that classic segmentation methods are prohibitively cumbersome, demanding automated data pipelines capable of dealing with non-trivial 3D images. Deep learning has demonstrated success in many image processing tasks, including material science applications, showing a promising alternative for a human-free segmentation pipeline. In this paper a modular interpretation of U-Net (Modular U-Net) is proposed and trained to segment 3D tomography images of a three-phased glass fiber-reinforced Polyamide 66. We compare 2D and 3D versions of our model, finding that the former is slightly better than the latter. We observe that human-comparable results can be achievied even with only 10 annotated layers and using a shallow U-Net yields better results than a deeper one. As a consequence, Neural Network (NN) show indeed a promising venue to automate XCT data processing pipelines needing no human, adhoc intervention.