most citedA novel unsupervised covid lung lesion segmentation based on the lung tissue identification

14 citations · 35 across the 6 of their papers we have counts for

collaborators

6 papers

eess.IV202214 cited

A novel unsupervised covid lung lesion segmentation based on the lung tissue identification

Faeze Gholamian Khah, Samaneh Mostafapour, Seyedjafar Shojaerazavi +2

This study aimed to evaluate the performance of a novel unsupervised deep learning-based framework for automated infections lesion segmentation from CT images of Covid patients. In…

physics.med-ph2021

The impact of MR-guided attenuation correction (compared to CTbased AC) on the diagnosis of anosmia based on 99m-Tc EthylCysteinate-Dimer SPECT images

Faeze Gholamiankhah, Samaneh Mostafapour, Seid Kazem Razavi-Ratki +2

99m-Tc Ethyl-Cysteinate-Dimer SPECT and MR imaging play a significant role in diagnosing anosmia. In this study, two-tissue class and three-tissue class attenuation maps (2C-MR and…

eess.IV20215 cited

Automated lung segmentation from CT images of normal and COVID-19 pneumonia patients

Faeze Gholamiankhah, Samaneh Mostafapour, Nouraddin Abdi Goushbolagh +4

Automated semantic image segmentation is an essential step in quantitative image analysis and disease diagnosis. This study investigates the performance of a deep learning-based mo…

physics.med-ph20213 cited

Deep learning-based synthetic CT generation from MR images: comparison of generative adversarial and residual neural networks

Faeze Gholamiankhah, Samaneh Mostafapour, Hossein Arabi

Currently, MRI-only radiotherapy (RT) eliminates some of the concerns about using CT images in RT chains such as the registration of MR images to a separate CT, extra dose delivery…

physics.med-ph20217 cited

Quantitative analysis of image quality in low-dose CT imaging for Covid-19 patients

Behrooz Ghane, Alireza Karimian, Samaneh Mostafapour +3

We set out to simulate four reduced dose-levels (60%-dose, 40%-dose, 20%-dose, and 10%-dose) of standard CT imaging using Beer-Lambert's law across 49 patients infected with COVID-…

physics.med-ph20216 cited

Deep learning-based attenuation correction in the image domain for myocardial perfusion SPECT imaging

Samaneh Mostafapour, Faeze Gholamiankhah, Sirvan Maroofpour +4

Objective: In this work, we set out to investigate the accuracy of direct attenuation correction (AC) in the image domain for the myocardial perfusion SPECT imaging (MPI-SPECT) usi…