Experts has a new look! Let us know what you think of the updates.

Provide feedback
Home
Scholarly Works
An Automated Deep Learning-Based Framework for...
Journal article

An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer

Abstract

Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor burden determinations. We developed and evaluated an automated deep learning (DL)-based framework that segments and classifies uptake on PSMA PET/CT. We identified 193 [18F] DCFPyL PET/CT scans of patients with biochemically recurrent prostate cancer from two institutions, including 137 [18F] DCFPyL PET/CT scans for training and internally …

Authors

Li Y; Imami MR; Zhao L; Amindarolzarbi A; Mena E; Leal J; Chen J; Gafita A; Voter AF; Li X

Journal

Journal of Imaging Informatics in Medicine, Vol. 37, No. 5, pp. 2206–2215

Publisher

Springer Nature

Publication Date

October 2024

DOI

10.1007/s10278-024-01104-y

ISSN

2948-2925