COVID-19 Volumetric Pulmonary Lesion Estimation on CT Images using a U-NET and Probabilistic Active Contour Segmentation

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 Volu...

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Bibliographic Details
Main Authors: Cendejas-Zaragoza, Leopoldo, Rodriguez-Obregon, Diomar E., Mejia-Rodriguez, Aldo R., Arce-Santana, Edgar R., Santos-Diaz, Alejandro
Format: Conference Proceeding
Language:English
Subjects:
Computed tomography
COVID-19
Estimation
Humans
Image segmentation
Lung
Lung - diagnostic imaging
Neural Networks, Computer
Pipelines
Probabilistic logic
SARS-CoV-2
Tomography, X-Ray Computed
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Description
Summary: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.
ISSN:2694-0604
DOI:10.1109/EMBC46164.2021.9629532