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

DOI: 10.1007/s00138-021-01196-4 DOI Help

Authors: Dimitrios Bellos (University of Nottingham) , Mark Basham (Diamond Light Source; Rosalind Franklin Institute) , Tony Pridmore (University of Nottingham) , Andrew P. French (University of Nottingham)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Machine Vision And Applications , VOL 32

State: Published (Approved)
Published: May 2021
Diamond Proposal Number(s): 9396

Open Access Open Access

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.

Journal Keywords: Deep learning; Semantic segmentation; CT denoising; Knowledge transfer; X-ray microtomography Sparse-angle tomography; Time-resolved tomography; Synthetic tomograms

Subject Areas: Information and Communication Technology


Instruments: I13-2-Diamond Manchester Imaging

Added On: 12/05/2021 15:54

Documents:
Bellos2021_Article_AStackedDenseDenoisingSegmenta.pdf

Discipline Tags:

Artificial Intelligence Information & Communication Technologies Data processing

Technical Tags:

Imaging Tomography