Skip to content

Anonymized dataset of COVID-19 cases with a focus on radiological imaging. This includes images (x-ray / ct) with extensive metadata, such as admission-, ICU-, laboratory-, and patient master-data.

License

Notifications You must be signed in to change notification settings

ml-workgroup/covid-19-image-repository

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

18 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

COVID-19 Image Repository

This project aims to create an anonymized data set of COVID-19 cases with a focus on radiological imaging. This includes images with extensive metadata, such as admission-, ICU-, laboratory-, and patient master-data.

This repository contains image data from the Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany.

Feature Set

id label unit comment / reference interval (precision)
1 patient_id randomly generated patient id
2 image_id randomly generated image id (filename)
3 sex m/w
4 age years currently redacted (see below)
5 size cm currently redacted (see below)
6 weight kg currently redacted (see below)
7 admission offset days days since admission (begin of symptoms)
8 icu admission offset days
9 death offset days days until death or null
10 modality currently all images are chest radiographs
11 projection ap, pa ...
12 lactate dehydrogenase U/l < 248 (5)
13 c-reactive protein mg/l <= 5 (1)
14 d-dimer mg/l 0 - 0.5 (0.1)
15 coagulation factor XIII % 70 - 140 (5)
16 neutrophils Tsd/µl 1.5 - 7.7 (0.1)
17 lymphocytes Tsd/µl 1.1 - 4.0 (0.1)
18 pO2 mmHg (5)
19 pCO2 mmHg (5)
20 corona test type

Age, size, and weight are currently redacted. We will publish this data when there are enough patients, that meaningful intervals can be chosen according to the concept of k-anonymity and l-diversity.

Offsets, i.e. admission offset and icu admission offset, are given as relative times in regard to the exam. Please consult the feature set table for units. I.e. an admission offset == -4 and icu admission offset == 6 would encode, that the patient was admitted to the hospital four days ago and was transferred to the ICU six days after the image was taken. Especially the admission offset can be noisy; please see FAQ #1.

All lab values (12 - 19) are given in intervals to protect patient identity. A value below the detection limit is denoted by -inf, above the detection limit by inf.

Download

We provide the raw, unprocessed, gray value image data as Nifti files. This is done to protect patient identity, as Dicom files are hard to anonymize. However, the files are too large to host on Github.

Additionally, we included downscaled versions of the Nifti images in the png folder.

Space and bandwith are kindly provided by the Open Telekom Cloud (Deutschen Telekom AG).

  • Version 2.0

  • Version 1.0

    Do not use this version (see erratum 1). The data is provided for reproducibility reasons.

Observations (FAQ)

  1. admission offset > icu admission offset
    Some of the cases suggest that the admission to the clinic was after admission to the ICU. This is an artifact due to using the specific (COVID) case data for determining the admission date (and therefore offset). The patient may have already been at the ICU before being diagnosed with COVID.

Errata

  1. In Version 1.0 there is an issue with the date calculation (issue #6). This might lead to incorrect date offset calculations. We strongly advise to upgrade to a newer version.

License and Attribution

Master-, image- and laboratory-data of this repository are licensed under the Creative Commons Attribution 3.0 Unported (CC BY 3.0).

If you use this data, you must attribute the authors in any publication (DOI: 10.6084/m9.figshare.12275009). You may include the specific release or commit hash for reproducibility.

Contact Information

Please use the ticketing system where applicable. Otherwise, please use the following EMail address: [email protected]

About

Anonymized dataset of COVID-19 cases with a focus on radiological imaging. This includes images (x-ray / ct) with extensive metadata, such as admission-, ICU-, laboratory-, and patient master-data.

Topics

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages