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

• URL: http://arxiv.org/abs/2306.05907v1
• Date: Fri, 9 Jun 2023 14:02:53 GMT
• Title: 2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning
• Authors: Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, Felix Lucka
• Abstract summary: We provide a versatile, open 2D fan-beam CT dataset suitable for developing machine learning techniques. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline.
• Score: 1.0266286487433585
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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