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

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 networ...

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Bibliographic Details
Published inScientific reports Vol. 13; no. 1; p. 1881
Main Authors Andriiashen, Vladyslav, van Liere, Robert, van Leeuwen, Tristan, Batenburg, K. Joost
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 02.02.2023
Nature Publishing Group
Nature Portfolio
Subjects
639/705/1042
639/766/400/1106
639/766/930/2735
Computed tomography
Humanities and Social Sciences
Image processing
Industrial applications
multidisciplinary
Neural networks
Quality control
Science
Science (multidisciplinary)
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-29079-w