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About:
Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients
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covidontheweb.inria.fr
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Academic Article
research paper
schema:ScholarlyArticle
isDefinedBy
Covid-on-the-Web dataset
title
Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients
Creator
Parker, William
Nicolaou, Savvas
Laradji, Issam
Lensink, Keegan
Barbano, Emilio
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source
ArXiv
abstract
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread into a global pandemic. A form of pneumonia, presenting as opacities with in a patient's lungs, is the most common presentation associated with this virus, and great attention has gone into how these changes relate to patient morbidity and mortality. In this work we provide open source models for the segmentation of patterns of pulmonary opacification on chest Computed Tomography (CT) scans which have been correlated with various stages and severities of infection. We have collected 663 chest CT scans of COVID-19 patients from healthcare centers around the world, and created pixel wise segmentation labels for nearly 25,000 slices that segment 6 different patterns of pulmonary opacification. We provide open source implementations and pre-trained weights for multiple segmentation models trained on our dataset. Our best model achieves an opacity Intersection-Over-Union score of 0.76 on our test set, demonstrates successful domain adaptation, and predicts the volume of opacification within 1.7/% of expert radiologists. Additionally, we present an analysis of the inter-observer variability inherent to this task, and propose methods for appropriate probabilistic approaches.
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2020-07-07
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arxiv
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09c4bd3bfa945f1f88206d8527326283e45d2e11
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Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients
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covid:09c4bd3bfa945f1f88206d8527326283e45d2e11#body_text
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named entity 'inherent'
named entity 'patient'
named entity 'slices'
named entity 'CT Scans'
named entity 'HEALTHCARE'
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