5 citations · 9 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…
Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses
Shadab Ahamed, Yixi Xu, Sara Kurkowska +12
This study addresses critical gaps in automated lymphoma segmentation from PET/CT images, focusing on issues often overlooked in existing literature. While deep learning has been a…
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…