A modular U-Net for automated segmentation of X-ray tomography images in composite materials

• URL: http://arxiv.org/abs/2107.07468v2
• Date: Sat, 2 Dec 2023 13:14:04 GMT
• Title: A modular U-Net for automated segmentation of X-ray tomography images in composite materials
• Authors: Jo\~ao P C Bertoldo, Etienne Decenci\`ere, David Ryckelynck, Henry Proudhon
• Abstract summary: Deep learning has demonstrated success in many image processing tasks, including material science applications. In this paper a modular interpretation of UNet is proposed and trained to segment 3D tomography images of a three-phased glass fiber-reinforced Polyamide 66. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 humanfree segmentation pipeline. In this paper a modular interpretation of UNet (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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