mlsanityData to 7 Oct 2026

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

arXiv 2107.07468 ↗First posted 15 Jul 2021eess.IV cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 49 references that are in the map. First posted on arXiv 15 Jul 2021.

Builds on

17 of 49 references.

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5 more
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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Leveraging Modified Ex Situ Tomography Data for Segmentation of In Situ Synchrotron X-Ray Computed Tomographycited by 1
  2. 2025Benchmarking machine learning models for multi-class state recognition in double quantum dot datacited by 1
  3. 2025Mitigating Pretraining-Induced Attention Asymmetry in 2D+ Electron Microscopy Image Segmentationcited by 0