tetano
Editor, Senior Moderator
BMC Res Notes
. 2021 May 12;14(1):178.
doi: 10.1186/s13104-021-05592-x.
COVID19-CT-dataset: an open-access chest CT image repository of 1000+ patients with confirmed COVID-19 diagnosis
Shokouh Shakouri[SUP] 1 [/SUP], Mohammad Amin Bakhshali[SUP] 1 [/SUP], Parvaneh Layegh[SUP] 2 [/SUP], Behzad Kiani[SUP] 1 [/SUP], Farid Masoumi[SUP] 1 [/SUP], Saeedeh Ataei Nakhaei[SUP] 3 [/SUP], Sayyed Mostafa Mostafavi[SUP] 4 [/SUP]
Affiliations
Abstract
Objectives: The ongoing Coronavirus disease 2019 (COVID-19) pandemic has drastically impacted the global health and economy. Computed tomography (CT) is the prime imaging modality for diagnosis of lung infections in COVID-19 patients. Data-driven and Artificial intelligence (AI)-powered solutions for automatic processing of CT images predominantly rely on large-scale, heterogeneous datasets. Owing to privacy and data availability issues, open-access and publicly available COVID-19 CT datasets are difficult to obtain, thus limiting the development of AI-enabled automatic diagnostic solutions. To tackle this problem, large CT image datasets encompassing diverse patterns of lung infections are in high demand.
Data description: In the present study, we provide an open-source repository containing 1000+ CT images of COVID-19 lung infections established by a team of board-certified radiologists. CT images were acquired from two main general university hospitals in Mashhad, Iran from March 2020 until January 2021. COVID-19 infections were ratified with matching tests including Reverse transcription polymerase chain reaction (RT-PCR) and accompanying clinical symptoms. All data are 16-bit grayscale images composed of 512 ? 512 pixels and are stored in DICOM standard. Patient privacy is preserved by removing all patient-specific information from image headers. Subsequently, all images corresponding to each patient are compressed and stored in RAR format.
Keywords: Artificial intelligence; COVID-19; Chest CT image; Clinical imaging; Computed tomography; Coronavirus; Deep learning; Diagnosis; Lung infection; Radiology.
. 2021 May 12;14(1):178.
doi: 10.1186/s13104-021-05592-x.
COVID19-CT-dataset: an open-access chest CT image repository of 1000+ patients with confirmed COVID-19 diagnosis
Shokouh Shakouri[SUP] 1 [/SUP], Mohammad Amin Bakhshali[SUP] 1 [/SUP], Parvaneh Layegh[SUP] 2 [/SUP], Behzad Kiani[SUP] 1 [/SUP], Farid Masoumi[SUP] 1 [/SUP], Saeedeh Ataei Nakhaei[SUP] 3 [/SUP], Sayyed Mostafa Mostafavi[SUP] 4 [/SUP]
Affiliations
- PMID: 33980279
- DOI: 10.1186/s13104-021-05592-x
Abstract
Objectives: The ongoing Coronavirus disease 2019 (COVID-19) pandemic has drastically impacted the global health and economy. Computed tomography (CT) is the prime imaging modality for diagnosis of lung infections in COVID-19 patients. Data-driven and Artificial intelligence (AI)-powered solutions for automatic processing of CT images predominantly rely on large-scale, heterogeneous datasets. Owing to privacy and data availability issues, open-access and publicly available COVID-19 CT datasets are difficult to obtain, thus limiting the development of AI-enabled automatic diagnostic solutions. To tackle this problem, large CT image datasets encompassing diverse patterns of lung infections are in high demand.
Data description: In the present study, we provide an open-source repository containing 1000+ CT images of COVID-19 lung infections established by a team of board-certified radiologists. CT images were acquired from two main general university hospitals in Mashhad, Iran from March 2020 until January 2021. COVID-19 infections were ratified with matching tests including Reverse transcription polymerase chain reaction (RT-PCR) and accompanying clinical symptoms. All data are 16-bit grayscale images composed of 512 ? 512 pixels and are stored in DICOM standard. Patient privacy is preserved by removing all patient-specific information from image headers. Subsequently, all images corresponding to each patient are compressed and stored in RAR format.
Keywords: Artificial intelligence; COVID-19; Chest CT image; Clinical imaging; Computed tomography; Coronavirus; Deep learning; Diagnosis; Lung infection; Radiology.