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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        <foaf:name>Frank Kramer</foaf:name>
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    <dct:title>Robust Chest CT Image Segmentation of COVID-19 Lung Infection based on limited data</dct:title>
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    <dcat:keyword>COVID-19</dcat:keyword>
    <dcat:keyword>segmentation</dcat:keyword>
    <dcat:keyword>computed tomography</dcat:keyword>
    <dcat:keyword>deep learning</dcat:keyword>
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    <dcat:keyword>clinical decision support</dcat:keyword>
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    <dct:description>&lt;p&gt;The coronavirus disease 2019 (COVID-19) affects billions of&lt;br&gt; lives around the world and has a significant impact on public&lt;br&gt; healthcare. Due to rising skepticism towards the sensitivity of&lt;br&gt; RT-PCR as screening method, medical imaging like computed&lt;br&gt; tomography offers great potential as alternative. For this&lt;br&gt; reason, automated image segmentation is highly desired as&lt;br&gt; clinical decision support for quantitative assessment and&lt;br&gt; disease monitoring. However, publicly available COVID-19&lt;br&gt; imaging data is limited which leads to overfitting of traditional&lt;br&gt; approaches. To address this problem, we propose an innovative&lt;br&gt; automated segmentation pipeline for COVID-19 infected&lt;br&gt; regions, which is able to handle small datasets by utilization as&lt;br&gt; variant databases. Our method focuses on on-the-fly&lt;br&gt; generation of unique and random image patches for training&lt;br&gt; by performing several preprocessing methods and exploiting&lt;br&gt; extensive data augmentation. For further reduction of the&lt;br&gt; overfitting risk, we implemented a standard 3D U-Net&lt;br&gt; architecture instead of new or computational complex neural&lt;br&gt; network architectures. Through a 5-fold cross-validation on 20&lt;br&gt; CT scans of COVID-19 patients, we were able to develop a&lt;br&gt; highly accurate as well as robust segmentation model for lungs&lt;br&gt; and COVID-19 infected regions without overfitting on the&lt;br&gt; limited data. Our method achieved Dice similarity coefficients&lt;br&gt; of 0.956 for lungs and 0.761 for infection. We demonstrated&lt;br&gt; that the proposed method outperforms related approaches,&lt;br&gt; advances the state-of-the-art for COVID-19 segmentation and&lt;br&gt; improves medical image analysis with limited data. The code&lt;br&gt; and model are available under the following link:&lt;br&gt; https://github.com/frankkramer-lab/covid19.MIScnn&lt;/p&gt;</dct:description>
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