Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 1-8
This upload contains samples 1 - 8 from the data collection described in Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", Sci Data 6, 215 (2019). https://doi.org/10.1038/s41597-019-0235-y or arXiv:1905.04787 (2019)
Abstract: "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."
The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by XRE nvand located in the FleX-ray Lab at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.
Please refer to the paper for all further technical details.
The complete data set can be found via the following links: 1-8, 9-16, 17-24, 25-32, 33-37, 38-42
The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on github
For more information or guidance in using these dataset, please get in touch with henri.dersarkissian [at] gmail.com Felix.Lucka [at] cwi.nl
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Der Sarkissian, H.H, Lucka, F, van Eijnatten, M.A.J.M, Colacicco, G, Coban, S.B, & Batenburg, K.J. (2019). Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 1-8. doi:10.5281/zenodo.2686726
|View at Zenodo - Samples 9-16|
|View at Zenodo - Samples 17-24|
|View at Zenodo - Samples 25-32|
|View at Zenodo - Samples 33-37|
|View at Zenodo - Samples 38-42|