LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction
Open Access
- 16 April 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Scientific Data
- Vol. 8 (1), 1-12
- https://doi.org/10.1038/s41597-021-00893-z
Abstract
Deep learning approaches for tomographic image reconstruction have become very effective and have been demonstrated to be competitive in the field. Comparing these approaches is a challenging task as they rely to a great extent on the data and setup used for training. With the Low-Dose Parallel Beam (LoDoPaB)-CT dataset, we provide a comprehensive, open-access database of computed tomography images and simulated low photon count measurements. It is suitable for training and comparing deep learning methods as well as classical reconstruction approaches. The dataset contains over 40000 scan slices from around 800 patients selected from the LIDC/IDRI database. The data selection and simulation setup are described in detail, and the generating script is publicly accessible. In addition, we provide a Python library for simplified access to the dataset and an online reconstruction challenge. Furthermore, the dataset can also be used for transfer learning as well as sparse and limited-angle reconstruction scenarios.Funding Information
- Deutsche Forschungsgemeinschaft (GRK 2224/1, GRK 2224/1, GRK 2224/1)
This publication has 41 references indexed in Scilit:
- The ASTRA Toolbox: A platform for advanced algorithm development in electron tomographyUltramicroscopy, 2015
- Deep Image Features in Music Information RetrievalInternational Journal of Electronics and Telecommunications, 2014
- Audio Features in Music Information RetrievalLecture Notes in Computer Science, 2014
- The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information RepositoryJournal of Digital Imaging, 2013
- The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT ScansMedical Physics, 2011
- Normalized CT Dose Index of the CT Scanners Used in the National Lung Screening TrialAmerican Journal of Roentgenology, 2010
- Creation of a CT Image Library for the Lung Screening Study of the National Lung Screening TrialJournal of Digital Imaging, 2006
- Image Quality Assessment: From Error Visibility to Structural SimilarityIEEE Transactions on Image Processing, 2004
- Development of a Digital Image Database for Chest Radiographs With and Without a Lung NoduleAmerican Journal of Roentgenology, 2000
- A NEW APPROACH TO CLASSIFICATION AND REGULARIZATION OF ILL-POSED OPERATOR EQUATIONS11This research was partially supported by the United States Army Research Office under grant DAAG-29-83-K-0109.Published by Elsevier BV ,1987