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

Type Dataset Dominik Müller, Iñaki Soto Rey, Frank Kramer (2020): Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net. Zenodo. Dataset. https://zenodo.org/record/3902293

Authors: Dominik Müller (IT-Infrastructure for Translational Medical Research, Faculty of Applied Computer Science, Faculty of Medicine, University of Augsburg, Germany) ; Iñaki Soto Rey (IT-Infrastructure for Translational Medical Research, Faculty of Applied Computer Science, Faculty of Medicine, University of Augsburg, Germany) ; Frank Kramer (IT-Infrastructure for Translational Medical Research, Faculty of Applied Computer Science, Faculty of Medicine, University of Augsburg, Germany) ;

Links

• Item record in Zenodo
• Digital object URL

Summary

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

More information

• DOI: 10.5281/zenodo.3902293

Subjects

• COVID-19, segmentation, computed tomography, deep learning, artificial intelligence, clinical decision support, medical image analysis

Dates

• Publication date: 2020
• Issued: June 29, 2020

Notes

Rights

• https://creativecommons.org/licenses/by/4.0/legalcode Creative Commons Attribution 4.0 International
• info:eu-repo/semantics/openAccess Open Access

Format

electronic resource

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