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>
    <dcat:keyword>artificial intelligence</dcat:keyword>
    <dcat:keyword>clinical decision support</dcat:keyword>
    <dcat:keyword>medical image analysis</dcat:keyword>
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    <dct:description>&lt;p&gt;&lt;strong&gt;Background:&lt;/strong&gt; 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.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Methods:&lt;/strong&gt; 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.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Results:&lt;/strong&gt; 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.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Conclusions:&lt;/strong&gt; 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.&lt;/p&gt; &lt;p&gt;The code 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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