5 citations · 15 across the 9 of their papers we have counts for
5 papers · 1 filter
Observer study-based evaluation of TGAN architecture used to generate oncological PET images
Roberto Fedrigo, Fereshteh Yousefirizi, Ziping Liu +9
The application of computer-vision algorithms in medical imaging has increased rapidly in recent years. However, algorithm training is challenging due to limited sample sizes, lack…
Lu SPECT Imaging in the Presence of Y: Does Y Degrade Image Quantification? A Simulation Study
Cassandra Miller, Carlos Uribe, Xinchi Hou +2
This work aims to investigate the accuracy of quantitative SPECT imaging of Lu in the presence of Y, which occurs in dual-isotope radiopharmaceutical therapy (RPT) i…
Semi-supervised learning towards automated segmentation of PET images with limited annotations: Application to lymphoma patients
Fereshteh Yousefirizi, Isaac Shiri, Joo Hyun O +9
The time-consuming task of manual segmentation challenges routine systematic quantification of disease burden. Convolutional neural networks (CNNs) hold significant promise to reli…
Convolutional neural network with a hybrid loss function for fully automated segmentation of lymphoma lesions in FDG PET images
Fereshteh Yousefirizi, Natalia Dubljevic, Shadab Ahamed +9
Segmentation of lymphoma lesions is challenging due to their varied sizes and locations in whole-body PET scans. This work presents a fully-automated segmentation technique using a…
Development of the Lymphatic System in the 4D XCAT Phantom
Roberto Fedrigo, William P. Segars, Patrick Martineau +4
Purpose: The XCAT phantom allows for highly sophisticated multimodality imaging research. It includes a complete set of organs, muscle, bone, soft tissue, while also accounting for…