Robust chest CT image segmentation of COVID-19 lung infection based on limited data.

ABSTRACT:

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 tr

SUBMITTER: Muller D 

PROVIDER: S-EPMC8313817 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature