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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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.4875738", 
  "title": "Robust Chest CT Image Segmentation of COVID-19 Lung Infection based on limited data", 
  "issued": {
    "date-parts": [
      [
        2021, 
        5, 
        30
      ]
    ]
  }, 
  "abstract": "<p><strong>Background:</strong> The coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. For quantitative assessment and disease monitoring medical imaging like computed tomography offers great potential as alternative to RT-PCR methods. For this reason, automated image segmentation is highly desired as clinical decision support. However, publicly available COVID-19 imaging data is limited which leads to overfitting of traditional approaches.</p>\n\n<p><strong>Methods:</strong> To address this problem, we propose an innovative automated segmentation pipeline for COVID-19 infected regions, which is able to handle small datasets by utilization as variant databases. Our method focuses on on-the-fly generation of unique and random image patches for training by performing several preprocessing methods and exploiting extensive data augmentation. For further reduction of the overfitting risk, we implemented a standard 3D U-Net architecture instead of new or computational complex neural network architectures.</p>\n\n<p><strong>Results:</strong> Through a k-fold cross-validation on 20 CT scans as training and validation of COVID-19, we were able to develop a highly accurate as well as robust segmentation model for lungs and COVID-19 infected regions without overfitting on limited data. We performed an in-detail analysis and discussion on the robustness of our pipeline through a sensitivity analysis based on the cross-validation and impact on model generalizability of applied preprocessing techniques. Our method achieved Dice similarity coefficients for COVID-19 infection between predicted and annotated segmentation from radiologists of 0.804 on validation and 0.661 on a separate testing set consisting of 100 patients.</p>\n\n<p><strong>Conclusions:</strong> We demonstrated that the proposed method outperforms related approaches, advances the state-of-the-art for COVID-19 segmentation and improves robust medical image analysis based on limited data.</p>\n\n<p>The code and model are available under the following link:<br>\nhttps://github.com/frankkramer-lab/covid19.MIScnn</p>", 
  "author": [
    {
      "family": "Dominik M\u00fcller"
    }, 
    {
      "family": "I\u00f1aki Soto Rey"
    }, 
    {
      "family": "Frank Kramer"
    }
  ], 
  "note": "Code: https://github.com/frankkramer-lab/covid19.MIScnn", 
  "version": "2.0", 
  "type": "dataset", 
  "id": "4875738"
}
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