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Dataset assetOpen Source CommunityMedical Imaging AnalysisCOVID‑19

COVID-19 Chest X-ray Segmentations Dataset

This dataset is a complete collection of COVID‑19 chest X‑ray segmentations, comprising 100 images. Each annotation file follows the COCO format and includes segmentations of anatomical categories (left lung, right lung, heart‑thorax, airway) and pathological categories (ground‑glass opacity, consolidation, pleural effusion, pneumothorax), as well as objects such as endotracheal tubes, central venous lines, monitoring probes, nasogastric tubes, chest tubes, and tubing. Every image was manually annotated by qualified radiologists.

Source
github
Created
Jul 17, 2020
Updated
Dec 8, 2023
Signals
128 views
Availability
Linked source ready
Overview

Dataset description and usage context

COVID-19 Chest X-ray Segmentations Dataset Overview

Dataset Summary

  • Name: COVID-19 Chest X-ray Segmentations Dataset
  • Description: This dataset contains segmentation data of chest X‑rays from COVID‑19 patients, intended to assist researchers in finding solutions during the pandemic. It comprises 100 images, each manually annotated by qualified radiologists.

Dataset Contents

  • Classes and Counts:
    • Left Lung: 99 images, 99 masks
    • Right Lung: 100 images, 100 masks
    • Heart and Mediastinum: 100 images, 100 masks
    • Airways: 99 images, 99 masks
    • Ground‑glass Opacity: 93 images, 93 masks
    • Consolidation: 32 images, 32 masks
    • Pleural Effusion: 2 images, 2 masks
    • Pneumothorax: 1 image, 1 mask
    • Endotracheal Tube: 13 images, 13 masks
    • Central Venous Line: 11 images, 11 masks
    • Monitoring Probe: 26 images, 26 masks
    • Nasogastric Tube: 10 images, 10 masks
    • Chest Tube: No data
    • Tubing: 16 images, 16 masks

Dataset Download

  • Access Methods:
    • ZIP format: Direct download
    • Git clone: Use the command git clone https://github.com/GeneralBlockchain/covid-19-chest-xray-segmentations-dataset.git

Usage Warning

  • Warning: Do not claim diagnostic performance of models without clinical studies.

License

  • License: Each image’s licensing information is contained in the metadata.csv file, including Apache 2.0, CC BY‑NC‑SA 4.0, and CC BY 4.0. The entire repository is released under the Attribution 4.0 International (CC BY 4.0) license.
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