5 citations · 15 across the 9 of their papers we have counts for
9 papers
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset
Shadab Ahamed, Yixi Xu, Ingrid Bloise +5
Automated slice classification is clinically relevant since it can be incorporated into medical image segmentation workflows as a preprocessing step that would flag slices with a h…
A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma
Shadab Ahamed, Natalia Dubljevic, Ingrid Bloise +6
Accurate detection and segmentation of diffuse large B-cell lymphoma (DLBCL) from PET images has important implications for estimation of total metabolic tumor volume, radiomics an…
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…
FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging
Teaghan O'Briain, Carlos Uribe, Kwang Moo Yi +3
To correct for respiratory motion in PET imaging, an interpretable and unsupervised deep learning technique, FlowNet-PET, was constructed. The network was trained to predict the op…
Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Features
Arman Rahmim, Amirhosein Toosi, Mohammad R. Salmanpour +9
Radiomics features extract quantitative information from medical images, towards the derivation of biomarkers for clinical tasks, such as diagnosis, prognosis, or treatment respons…
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…