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20182021
most citedAutomated Quantification of CT Patterns Associated with COVID-19 from Chest CT

156 citations · 312 across the 11 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.CV2020★ 78 cited

Event-based Motion Segmentation with Spatio-Temporal Graph Cuts

Yi Zhou, Guillermo Gallego, Xiuyuan Lu +2

Identifying independently moving objects is an essential task for dynamic scene understanding. However, traditional cameras used in dynamic scenes may suffer from motion blur or ex…

eess.IV2020★ 5 cited

Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based ground-truth

Eduardo Mortani Barbosa, Warren B. Gefter, Rochelle Yang +13

Purpose: To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiog…

eess.IV2020★ 20 cited

3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19

Siqi Liu, Bogdan Georgescu, Zhoubing Xu +10

The Coronavirus Disease (COVID-19) has affected 1.8 million people and resulted in more than 110,000 deaths as of April 12, 2020. Several studies have shown that tomographic patter…

eess.IV2020★ 156 cited

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

Shikha Chaganti, Abishek Balachandran, Guillaume Chabin +17

Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities…

eess.IV2020★ 8 cited

Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images

Donghao Zhang, Siqi Liu, Shikha Chaganti +5

With the injection of contrast material into blood vessels, multi-phase contrasted CT images can enhance the visibility of vessel networks in the human body. Reconstructing the 3D…

eess.IV2020★ 4 cited

No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks

Siqi Liu, Arnaud Arindra Adiyoso Setio, Florin C. Ghesu +4

Detecting malignant pulmonary nodules at an early stage can allow medical interventions which may increase the survival rate of lung cancer patients. Using computer vision techniqu…