Computing infection distributions and longitudinal evolution patterns in lung CT images.

ABSTRACT:

Background

Spatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template.

Methods

A VB-Net is designed to automatically segment infection regions in CT images. After training and validating the model, we segmented all the CT images in the study. The segmentation results are then warped onto a pre-defined template CT image using deformable registration based on lung fields. Then, the spatial distributions of infection regions and those during the course of the disease are calculated at

SUBMITTER: Gu D 

PROVIDER: S-EPMC7987127 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature