Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN.

ABSTRACT:

Background

Lesion segmentation is a critical step in medical image analysis, and methods to identify pathology without time-intensive manual labeling of data are of utmost importance during a pandemic and in resource-constrained healthcare settings. Here, we describe a method for fully automated segmentation and quantification of pathological COVID-19 lung tissue on chest Computed Tomography (CT) scans without the need for manually segmented training data.

Methods

We trained a cycle-consistent generative adversarial network (CycleGAN) to convert images of COVID-19 scans into their generated healthy equivalents. Subtraction of the generated healthy images from their corresponding original CT scans yielded maps of pathological tissue, without background lung parenchyma, fissur

SUBMITTER: Connell M 

PROVIDER: S-EPMC9288584 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature