2DeteCT -- A large 2D expandable, trainable, experimental Computed
  Tomography dataset for machine learning
Maximilian B. Kiss Sophia B. Coban K. Joost Batenburg Tristan van Leeuwen Felix Lucka
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 (5000 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.