Demonstration of efficient transfer learning in segmentation problem in synchrotron radiation X-ray CT data for epoxy resin

Abstract

Synchrotron radiation X-ray computed tomography (CT) provides information about the three-dimensional electron density inside a sample with a high spatial resolution. Recently, the need to examine the internal structure of materials composed of light elements, such as water and carbon fibers in resins, has increased. Small density differences in these systems give low X-ray contrast; segmentation methods suited for this type of problem are necessary. Machine learning is typically used to analyze CT data, and a large amount of training data is required to train a machine learning model. Conversely, transfer learning, which uses existing learning models, can develop a learning model using only a small amount of training data. In this study, the synchrotron radiation X-ray CT images of an epoxy resin containing water have been analyzed using transfer learning as the validation of a method for analyzing low-contrast CT data with high accuracy. Circular domain structures in the resin have been observed using the X-ray CT method, and statistical information about these structures has been successfully obtained by transfer learning-based analysis. Here, transfer learning is performed using twelve slices within an X-ray CT 3D image, demonstrating that low-contrast synchrotron CT data can be segmented with a small amount of training data.

Keywords

• machine learning
• transfer learning
• x-ray ct
• polymer