CT-based data generation for foreign object detection on a single X-ray projection

Scientific Reports, vol. 13, pp. 1881

Abstract

Although X-ray imaging is used routinely in industry for high-throughput product quality control, its capability to detect internal defects has strong limitations. The main challenge stems from the superposition of multiple object features within a single X-ray view. Deep Convolutional neural networks can be trained by annotated datasets of X-ray images to detect foreign objects in real-time. However, this approach depends heavily on the availability of a large amount of data, strongly hampering the viability of industrial use with high variability between batches of products. We present a computationally efficient, CT-based approach for creating artificial single-view X-ray data based on just a few physically CT-scanned objects. By algorithmically modifying the CT-volume, a large variety of training examples is obtained. Our results show that applying the generative model to a single CT-scanned object results in image analysis accuracy that would otherwise be achieved with scans of tens of real-world samples. Our methodology leads to a strong reduction in training data needed, improved coverage of the combinations of base and foreign objects, and extensive generalizability to additional features. Once trained on just a single CT-scanned object, the resulting deep neural network can detect foreign objects in real-time with high accuracy.

Authors 4

1. Vladyslav Andriiashen corresponding

   Centrum Wiskunde & Informatica

   Affiliation as printed

   Computational Imaging, Centrum Wiskunde en Informatica, Science Park 123, 1098 XG, Amsterdam, The Netherlands. vladyslav.andriiashen@cwi.nl

   Computational Imaging, Centrum Wiskunde en Informatica, Science Park 123, 1098 XG, Amsterdam, The Netherlands

2. Robert van Liere

   Centrum Wiskunde & Informatica · Eindhoven University of Technology

   Affiliation as printed

   Computational Imaging, Centrum Wiskunde en Informatica, Science Park 123, 1098 XG, Amsterdam, The Netherlands

   Faculteit Wiskunde en Informatica, Technical University Eindhoven, Groene Loper 5, 5612 AZ , Eindhoven, The Netherlands

3. Tristan van Leeuwen

   Centrum Wiskunde & Informatica · Utrecht University

   Affiliation as printed

   Computational Imaging, Centrum Wiskunde en Informatica, Science Park 123, 1098 XG, Amsterdam, The Netherlands

   Mathematical Institute, Utrecht University, Budapestlaan 6, 3584 CD , Utrecht, The Netherlands

4. Kees Joost Batenburg Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Centrum Wiskunde & Informatica

   Affiliation as printed

   Computational Imaging, Centrum Wiskunde en Informatica, Science Park 123, 1098 XG, Amsterdam, The Netherlands

   Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands