Title: Robust Chest CT Image Segmentation of COVID-19 Lung Infection based on limited data

Type Dataset Dominik Müller, Iñaki Soto Rey, Frank Kramer (2021): Robust Chest CT Image Segmentation of COVID-19 Lung Infection based on limited data. Zenodo. Dataset. https://zenodo.org/record/4875738

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

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

The code and model are available under the following link: https://github.com/frankkramer-lab/covid19.MIScnn

More information

• DOI: 10.5281/zenodo.4875738

Subjects

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

Dates

• Publication date: 2021
• Issued: May 30, 2021

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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