A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms

Bellos, Dimitrios; Basham, Mark; Pridmore, Tony; French, Andrew P.

Abstract

Over recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always available. When the X-ray exposure time has to be limited, undersampled tomograms (in terms of their component projections) are attained. This low number of projections offers low-quality reconstructions that are difficult to segment. Here, we consider CT time-series (i.e. 4D data), where the limited time for capturing fast-occurring temporal events results in the time-series tomograms being necessarily undersampled. Fortunately, in these collections, it is common practice to obtain representative highly sampled tomograms before or after the time-critical portion of the experiment. In this paper, we propose an end-to-end network that can learn to denoise and segment the time-series’ undersampled CTs, by training with the earlier highly sampled representative CTs. Our single network can offer two desired outputs while only training once, with the denoised output improving the accuracy of the final segmentation. Our method is able to outperform state-of-the-art methods in the task of semantic segmentation and offer comparable results in regard to denoising. Additionally, we propose a knowledge transfer scheme using synthetic tomograms. This not only allows accurate segmentation and denoising using less real-world data, but also increases segmentation accuracy. Finally, we make our datasets, as well as the code, publicly available.

Citation

Bellos, D., Basham, M., Pridmore, T., & French, A. P. (2021). A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms. Machine Vision and Applications, 32(3), Article 75. https://doi.org/10.1007/s00138-021-01196-4

• Journal Article Type: Article
• Acceptance Date: Mar 12, 2021
• Online Publication Date: Apr 27, 2021
• Publication Date: May 1, 2021
• Deposit Date: Apr 13, 2021
• Publicly Available Date: Apr 27, 2021
• Journal: Machine Vision and Applications
• Print ISSN: 0932-8092
• Electronic ISSN: 1432-1769
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 32
• Issue: 3
• Article Number: 75
• DOI: https://doi.org/10.1007/s00138-021-01196-4
• Public URL: https://nottingham-repository.worktribe.com/output/5463829
• Publisher URL: https://link.springer.com/article/10.1007/s00138-021-01196-4