activity
20232026
most citedHow 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

2 citations · 3 across the 4 of their papers we have counts for

collaborators

4 papers

physics.med-ph2026

Beyond the Tumor: Recurrence-Prone Radiomics for Prognostication in Negative PSMA PET/CT scans of Prostate Cancer

Fereshteh Yousefirizi, Sara Harsini, Mobin Mohebi +14

In patients with biochemical recurrence of prostate cancer and negative PSMA PET/CT, radiomics features extracted from recurrence-prone organs can predict clinical progression and…

eess.IV2025

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…

physics.med-ph2024★ 2 cited

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…

physics.med-ph2023★ 1 cited

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…