abstract

• A two-step method for obtaining a volumetric estimation of COVID-19 related lesion from CT images is proposed. The first step consists in applying a U-NET convolutional neural network to provide a segmentation of the lung-parenchyma. This architecture is trained and validated using the Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines (PleThora) dataset, which is publicly available. The second step consists in obtaining the volumetric lesion estimation using an automatic algorithm based on a probabilistic active contour (PACO) region delimitation approach. Our pipeline successfully segmented COVID-19 related lesions in CT images, with exception of some mislabeled regions including lung airways and vasculature. Our workflow was applied to images in a cohort of 50 patients. © 2021 IEEE.

authors

• Arce Santana Edgar Román
• Mejía Rodríguez Aldo Rodrigo
• Rodríguez-Obregón D.E.

publication date

• 2021-01-01

published in

• Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS  Proceedings

keywords

• diagnostic imaging; human; lung; x-ray computed tomography; COVID-19; Humans; Lung; Neural Networks, Computer; SARS-CoV-2; Tomography, X-Ray Computed

Digital Object Identifier (DOI)

• 10.1109/EMBC46164.2021.9629532

PubMed ID

start page

• 3850

end page

• 3853

volume

issue