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Dataset Open Access

Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net

Dominik Müller; Iñaki Soto Rey; Frank Kramer

The coronavirus disease 2019 (COVID-19) affects billions of
lives around the world and has a significant impact on public
healthcare. Due to rising skepticism towards the sensitivity of
RT-PCR as screening method, medical imaging like computed
tomography offers great potential as alternative. For this
reason, automated image segmentation is highly desired as
clinical decision support for quantitative assessment and
disease monitoring. However, publicly available COVID-19
imaging data is limited which leads to overfitting of traditional
approaches. 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. Through a 5-fold cross-validation on 20
CT scans of COVID-19 patients, we were able to develop a
highly accurate as well as robust segmentation model for lungs
and COVID-19 infected regions without overfitting on the
limited data. Our method achieved Dice similarity coefficients
of 0.956 for lungs and 0.761 for infection. We demonstrated
that the proposed method outperforms related approaches,
advances the state-of-the-art for COVID-19 segmentation and
improves medical image analysis with limited data. The code
and model are available under the following link:

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