  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:
https://github.com/frankkramer-lab/covid19.MIScnn
