ArticleScientific data2019
A cone-beam X-ray computed tomography data collection designed for machine learning.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Quantitative stability of CT numbers in novel cone-beam CT: An intra-institution cross-scanner and longitudinal analysis.Journal of applied clinical medical physics · 2026Article
- DiffPlantCT: a training-free, annotation-free approach to cross-species plant CT image segmentation.Plant methods · 2026Article
- A cone-beam photon-counting CT dataset for spectral image reconstruction and deep learning.Scientific data · 2025Article
- Sliding volume-based streak artifact reduction network (S-STAR Net) for ultra-sparse-view computed tomography.BMC medical imaging · 2025Article
- Point-cloud segmentation with in-silico data augmentation for prostate cancer treatment.Medical physics · 2025Article
- Application of machine learning in dentistry: insights, prospects and challenges.Acta odontologica Scandinavica · 2025Review
- A high-quality dataset featuring classified and annotated cervical spine X-ray atlas.Scientific data · 2024Article
- Sinogram domain angular upsampling of sparse-view micro-CT with dense residual hierarchical transformer and attention-weighted loss.Computer methods and programs in biomedicine · 2023Article
- Foam-like phantoms for comparing tomography algorithms.Journal of synchrotron radiation · 2022Article
- LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction.Scientific data · 2021Article
- Multi-Scale Learned Iterative Reconstruction.IEEE transactions on computational imaging · 2020Article
- 3D characterization of walnut morphological traits using X-ray computed tomography.Plant methods · 2020Article
- A cone-beam X-ray computed tomography data collection designed for machine learning.Scientific data · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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 set-up 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 source 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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.