Encoder-decoder convolutional neural network for simple CT segmentation of COVID-19 infected lungs.

ABSTRACT: This work presents the application of an Encoder-Decoder convolutional neural network (ED-CNN) model to automatically segment COVID-19 computerised tomography (CT) data. By doing so we are producing an alternative model to current literature, which is easy to follow and reproduce, making it more accessible for real-world applications as little training would be required to use this. Our simple approach achieves results comparable to those of previously published studies, which use more complex deep-learning networks. We demonstrate a high-quality automated segmentation prediction of thoracic CT scans that correctly delineates the infected regions of the lungs. This segmentation automation can be used as a tool to speed up the contouring process, either to check manual contouring in place o

SUBMITTER: Newson KS 

PROVIDER: S-EPMC11323195 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature