activity
20182021
most cited3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19

20 citations · 34 across the 4 of their papers we have counts for

collaborators

9 papers

eess.IV20214 cited

Single Neuron Segmentation using Graph-based Global Reasoning with Auxiliary Skeleton Loss from 3D Optical Microscope Images

Heng Wang, Yang Song, Chaoyi Zhang +4

One of the critical steps in improving accurate single neuron reconstruction from three-dimensional (3D) optical microscope images is the neuronal structure segmentation. However,…

eess.IV20205 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.IV202020 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

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

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

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…