Dual Energy CT Material Segmentation using Convolutional Neural Networks

抄録

Material Segmentation for industrial 3D CT scans are critical for obtaining more coherent visualizations of a scanned object.Although manual and semi-automated segmentation methods exist, they tend to be time-consuming and require expertise. The theme of this research is to explore the possibility of implementing Convolutional Neural Networks (CNNs) to automate the segmentation process of a prespecified set of materials. However, one common obstacle associated with CNNs is the amount of training data it requires to converge; in addition, 3D CT data is scarce and difficult to obtain. To overcome this, this paper will adopt U-Net which is a convolutional network designed for biomedical image segmentation. U-Net presents a novel method to obtain 2D segmentations with very few training images. New architectures based on U-Net will be developed to exploit the spectral information from dual-energy CT to improve material segmentation accuracy.