Intraprostatic Tumor Segmentation on PSMA PET Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network.
PSMA-PET
convolutional neuronal networks
histopathology
prostate cancer
segmentation
Journal
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
ISSN: 1535-5667
Titre abrégé: J Nucl Med
Pays: United States
ID NLM: 0217410
Informations de publication
Date de publication:
01 06 2021
01 06 2021
Historique:
received:
31
07
2020
accepted:
07
10
2020
pubmed:
1
11
2020
medline:
27
8
2021
entrez:
31
10
2020
Statut:
ppublish
Résumé
Accurate delineation of the intraprostatic gross tumor volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific membrane antigen PET (PSMA PET) may outperform MRI in GTV detection. However, visual GTV delineation underlies interobserver heterogeneity and is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for automated segmentation of intraprostatic tumor (GTV-CNN) in PSMA PET.
Identifiants
pubmed: 33127624
pii: jnumed.120.254623
doi: 10.2967/jnumed.120.254623
pmc: PMC8729869
doi:
Substances chimiques
Gallium Isotopes
0
Gallium Radioisotopes
0
gallium 68 PSMA-11
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
823-828Informations de copyright
COPYRIGHT © 2021 by the Society of Nuclear Medicine and Molecular Imaging.
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