A modular U-Net for automated segmentation of X-ray tomography images in composite materials
This work addresses the need for automated data pipelines in material science to handle fast-acquired high-resolution images, though it is incremental as it adapts existing U-Net architectures.
The authors tackled the problem of automating segmentation of 3D X-ray tomography images for composite materials, proposing a modular U-Net that achieved human-comparable results with only 10 annotated layers and found that a shallow version outperformed a deeper one.
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.