Dataset Open Access

Robust Chest CT Image Segmentation of COVID-19 Lung Infection based on limited data

Dominik Müller; Iñaki Soto Rey; Frank Kramer


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{
  "description": "<p>The coronavirus disease 2019 (COVID-19) affects billions of<br>\nlives around the world and has a significant impact on public<br>\nhealthcare. Due to rising skepticism towards the sensitivity of<br>\nRT-PCR as screening method, medical imaging like computed<br>\ntomography offers great potential as alternative. For this<br>\nreason, automated image segmentation is highly desired as<br>\nclinical decision support for quantitative assessment and<br>\ndisease monitoring. However, publicly available COVID-19<br>\nimaging data is limited which leads to overfitting of traditional<br>\napproaches. To address this problem, we propose an innovative<br>\nautomated segmentation pipeline for COVID-19 infected<br>\nregions, which is able to handle small datasets by utilization as<br>\nvariant databases. Our method focuses on on-the-fly<br>\ngeneration of unique and random image patches for training<br>\nby performing several preprocessing methods and exploiting<br>\nextensive data augmentation. For further reduction of the<br>\noverfitting risk, we implemented a standard 3D U-Net<br>\narchitecture instead of new or computational complex neural<br>\nnetwork architectures. Through a 5-fold cross-validation on 20<br>\nCT scans of COVID-19 patients, we were able to develop a<br>\nhighly accurate as well as robust segmentation model for lungs<br>\nand COVID-19 infected regions without overfitting on the<br>\nlimited data. Our method achieved Dice similarity coefficients<br>\nof 0.956 for lungs and 0.761 for infection. We demonstrated<br>\nthat the proposed method outperforms related approaches,<br>\nadvances the state-of-the-art for COVID-19 segmentation and<br>\nimproves medical image analysis with limited data. The code<br>\nand model are available under the following link:<br>\nhttps://github.com/frankkramer-lab/covid19.MIScnn</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "IT-Infrastructure for Translational Medical Research, Faculty of Applied Computer Science, Faculty of Medicine, University of Augsburg, Germany", 
      "@id": "https://orcid.org/0000-0003-0838-9885", 
      "@type": "Person", 
      "name": "Dominik M\u00fcller"
    }, 
    {
      "affiliation": "IT-Infrastructure for Translational Medical Research, Faculty of Applied Computer Science, Faculty of Medicine, University of Augsburg, Germany", 
      "@id": "https://orcid.org/0000-0003-3061-5818", 
      "@type": "Person", 
      "name": "I\u00f1aki Soto Rey"
    }, 
    {
      "affiliation": "IT-Infrastructure for Translational Medical Research, Faculty of Applied Computer Science, Faculty of Medicine, University of Augsburg, Germany", 
      "@id": "https://orcid.org/0000-0002-2857-7122", 
      "@type": "Person", 
      "name": "Frank Kramer"
    }
  ], 
  "url": "https://zenodo.org/record/4279398", 
  "datePublished": "2020-06-29", 
  "version": "1.0", 
  "keywords": [
    "COVID-19", 
    "segmentation", 
    "computed tomography", 
    "deep learning", 
    "artificial intelligence", 
    "clinical decision support", 
    "medical image analysis"
  ], 
  "@context": "https://schema.org/", 
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  "identifier": "https://doi.org/10.5281/zenodo.4279398", 
  "@id": "https://doi.org/10.5281/zenodo.4279398", 
  "@type": "Dataset", 
  "name": "Robust Chest CT Image Segmentation of COVID-19 Lung Infection based on limited data"
}
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