8 citations · 25 across the 8 of their papers we have counts for
8 papers
Comprehensive Evaluation of Quantitative Measurements from Automated Deep Segmentations of PSMA PET/CT Images
Obed Korshie Dzikunu, Amirhossein Toosi, Shadab Ahamed +4
This study performs a comprehensive evaluation of quantitative measurements as extracted from automated deep-learning-based segmentation methods, beyond traditional Dice Similarity…
Uncertainty Propagation from Projections to Region Counts in Tomographic Imaging: Application to Radiopharmaceutical Dosimetry
Lucas Polson, Sara Kurkowska, Chenguang Li +6
Radiopharmaceutical therapies (RPTs) present a major opportunity to improve cancer therapy. Although many current RPTs use the same injected activity for all patients, there is int…
How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion Models
Amirhosein Toosi, Sara Harsini, François Bénard +2
Prostate specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) imaging provides a tremendously exciting frontier in visualization of prostate c…
Investigating heterogeneous PSMA ligand uptake inside parotid glands
Caleb Sample, Carlos Uribe, Arman Rahmim +3
The purpose was to investigate the spatial heterogeneity of prostate-specific membrane antigen (PSMA) positron emission tomography (PET) uptake within parotid glands. We aim to qua…
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…
PSMA PET/CT as a predictive tool for sub-regional importance estimates in the parotid gland
Caleb Sample, Arman Rahmim, François Bénard +2
Xerostomia and radiation-induced salivary gland dysfunction remain a common side effect for head-and-neck radiotherapy patients, and attempts have been made to quantify the heterog…
Neural blind deconvolution for deblurring and supersampling PSMA PET
Caleb Sample, Arman Rahmim, Carlos Uribe +4
Objective: To simultaneously deblur and supersample prostate specific membrane antigen (PSMA) positron emission tomography (PET) images using neural blind deconvolution. Approach:…
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…