Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients

1 code implementation • 7 Jul 2020 • Keegan Lensink, Issam Laradji, Marco Law, Paolo Emilio Barbano, Savvas Nicolaou, William Parker, Eldad Haber

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.

Computed Tomography (CT) Domain Adaptation +1

Machine Learning Automatically Detects COVID-19 using Chest CTs in a Large Multicenter Cohort

no code implementations • 9 Jun 2020 • Eduardo Jose Mortani Barbosa Jr., Bogdan Georgescu, Shikha Chaganti, Gorka Bastarrika Aleman, Jordi Broncano Cabrero, Guillaume Chabin, Thomas Flohr, Philippe Grenier, Sasa Grbic, Nakul Gupta, François Mellot, Savvas Nicolaou, Thomas Re, Pina Sanelli, Alexander W. Sauter, Youngjin Yoo, Valentin Ziebandt, Dorin Comaniciu

Training/testing cohorts included 927/100 COVID-19, 388/33 ILD, 189/33 other pneumonias, and 559/34 normal (no pathologies) CTs.

BIG-bench Machine Learning Clustering +2

Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT

no code implementations • 2 Apr 2020 • Shikha Chaganti, Abishek Balachandran, Guillaume Chabin, Stuart Cohen, Thomas Flohr, Bogdan Georgescu, Philippe Grenier, Sasa Grbic, Si-Qi Liu, François Mellot, Nicolas Murray, Savvas Nicolaou, William Parker, Thomas Re, Pina Sanelli, Alexander W. Sauter, Zhoubing Xu, Youngjin Yoo, Valentin Ziebandt, Dorin Comaniciu

Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations.