2DeteCT – A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5,000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline.

The seminar will be held in a hybrid format. We strongly encourage you to participate in person at MR 11 , Centre of Mathematical Sciences, CB3 0WA .

Alternatively, please join using the following Zoom link:

Join Zoom Meeting: https://maths-cam-ac-uk.zoom.us/j/93331132587?pwd=MlpReFY3MVpyVThlSi85TmUzdTJxdz09

• Speaker: Maximilian Kiss, Centrum Wiskunde & Informatica
• Wednesday 15 November 2023, 13:00–14:00
• Venue: Centre for Mathematical Sciences, MR11.
• Series: CMIH Hub seminar series; organiser: Yuan Huang.