Original Research ARTICLE
Machine learning techniques for the segmentation of tomographic image data of functional materials
- 1Institute of Stochastics, University of Ulm, Germany
- 2Institute of Functional Nanosystems, University of Ulm, Germany
In this paper, various kinds of applications are presented, in which tomographic image data depicting microstructures of materials are semantically segmented by combining machine learning methods and conventional image processing steps.
The main focus of this paper is the grain-wise segmentation of time-resolved CT data of an AlCu specimen which was obtained in between several Ostwald ripening steps. The poorly visible grain boundaries in 3D CT data were enhanced using convolutional neural networks (CNNs). The CNN architectures considered in this paper are a 2D U-Net, a multichannel 2D U-Net and a 3D U-Net where the latter was trained at a lower resolution due to memory limitations. For training the CNNs, ground truth information was derived from 3D X-ray diffraction (3DXRD) measurements. The grain boundary images enhanced by the CNNs were then segmented using a marker-based watershed algorithm with an additional postprocessing step for reducing oversegmentation. The segmentation results obtained by this procedure were quantitatively compared to ground truth information derived by the 3DXRD measurements. A quantitative comparison between segmentation results indicates that the 3D U-Net performs best among the considered U-Net architectures.
Additionally, a scenario, in which "ground truth" data
is only available in one time step, is considered. Therefore,
a CNN was trained only with CT and 3DXRD data from
the last measured time step. The trained network and the image processing steps were then applied to the entire series of CT scans. The resulting segmentations exhibited a similar quality compared to those obtained by the network which was trained with the entire series of CT scans.
Keywords: machine learning, segmentation, X-Ray Microtomography, Polycrystalline microstructure, ostwald ripening, statistical image analysis
Received: 04 Feb 2019;
Accepted: 07 Jun 2019.
Edited by:Benjamin Klusemann, Leuphana University, Germany
Reviewed by:Stefan Sandfeld, Freiberg University of Mining and Technology, Germany
Tim Dahmen, German Research Centre for Artificial Intelligence, Germany
Copyright: © 2019 Furat, Wang, Neumann, Petrich, Weber, Krill III and Schmidt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mr. Orkun Furat, University of Ulm, Institute of Stochastics, Ulm, Germany, firstname.lastname@example.org