ArticleBioengineering (Basel, Switzerland)2021
An Open-Source COVID-19 CT Dataset with Automatic Lung Tissue Classification for Radiomics.
Article in Bioengineering (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 40 citations in OpenAlex.
- LobePrior segments lung lobes on computed tomography images in the presence of severe abnormalities.Scientific reports · 2026Article
- A comparative study of artifact reduction techniques in metal-implanted CT scans.International journal of physiology, pathophysiology and pharmacology · 2026Review
- Radiomic Feature Characteristics of Ovine Pulmonary Adenocarcinoma.Veterinary sciences · 2025Article
- Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies.Diagnostics (Basel, Switzerland) · 2024Review
- Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT.Scientific reports · 2024Article
- Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19) patients.Journal of thoracic disease · 2024Article
- HRCTCov19-a high-resolution chest CT scan image dataset for COVID-19 diagnosis and differentiation.BMC research notes · 2024Article
- Article
- How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications.Bioengineering (Basel, Switzerland) · 2023Review
- A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images.Diagnostics (Basel, Switzerland) · 2023Review
- From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases.Research square · 2023Article
- Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis.Nature machine intelligence · 2023Article
- Deep Learning for Detecting COVID-19 Using Medical Images.Bioengineering (Basel, Switzerland) · 2022Article
- Robust Medical Image Colorization with Spatial Mask-Guided Generative Adversarial Network.Bioengineering (Basel, Switzerland) · 2022Article
- Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches-an international, multi-center comparative study.Quantitative imaging in medicine and surgery · 2022Article
- Lesion segmentation in lung CT scans using unsupervised adversarial learning.Medical & biological engineering & computing · 2022Article
- Comprehensive Survey of Machine Learning Systems for COVID-19 Detection.Journal of imaging · 2022Review
- Efficient analysis of COVID-19 clinical data using machine learning models.Medical & biological engineering & computing · 2022Article
- Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks.Computers in biology and medicine · 2022Review
- Object or Background: An Interpretable Deep Learning Model for COVID-19 Detection from CT-Scan Images.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The coronavirus disease 19 (COVID-19) pandemic is having a dramatic impact on society and healthcare systems. In this complex scenario, lung computerized tomography (CT) may play an important prognostic role. However, datasets released so far present limitations that hamper the development of tools for quantitative analysis. In this paper, we present an open-source lung CT dataset comprising information on 50 COVID-19-positive patients. The CT volumes are provided along with (i) an automatic threshold-based annotation obtained with a Gaussian mixture model (GMM) and (ii) a scoring provided by an expert radiologist. This score was found to significantly correlate with the presence of ground glass opacities and the consolidation found with GMM. The dataset is freely available in an ITK-based file format under the CC BY-NC 4.0 license. The code for GMM fitting is publicly available, as well. We believe that our dataset will provide a unique opportunity for researchers working in the field of medical image analysis, and hope that its release will lay the foundations for the successfully implementation of algorithms to support clinicians in facing the COVID-19 pandemic.
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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.