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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.
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Contributor : Henry Proudhon Connect in order to contact the contributor
Submitted on : Monday, January 3, 2022 - 3:21:23 PM
Last modification on : Saturday, October 22, 2022 - 5:10:28 AM


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João P C Bertoldo, Etienne Decencière, David Ryckelynck, Henry Proudhon. A modular U-Net for automated segmentation of X-ray tomography images in composite materials. Frontiers in Materials, 2021, 8, pp.761229. ⟨10.21203/⟩. ⟨hal-03380106v2⟩



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