Automated deep learning-based segmentation of COVID-19 lesions from chest computed tomography images.

ABSTRACT:

Purpose

The novel coronavirus COVID-19, which spread globally in late December 2019, is a global health crisis. Chest computed tomography (CT) has played a pivotal role in providing useful information for clinicians to detect COVID-19. However, segmenting COVID-19-infected regions from chest CT results is challenging. Therefore, it is desirable to develop an efficient tool for automated segmentation of COVID-19 lesions using chest CT. Hence, we aimed to propose 2D deep-learning algorithms to automatically segment COVID-19-infected regions from chest CT slices and evaluate their performance.

Material and methods

Herein, 3 known deep learning networks: U-Net, U-Net++, and Res-Unet, were trained from scratch for automated segmenting of COVID-19 lesions using chest CT images. Th

SUBMITTER: Salehi M 

PROVIDER: S-EPMC9453472 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature