5 citations · 13 across the 4 of their papers we have counts for
4 papers
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…
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…