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Evaluation of Semiautomatic and Deep Learning-Based Fully Automatic Segmentation Methods on [F-18]Fdg Pet/Ct Images from Patients with Lymphoma: Influence on Tumor Characterization

AuthID
P-00Y-AY3

Author(s)

Constantino, CS

Leocadio, S

Oliveira, FPM

Silva, M

Oliveira, C

Castanheira, JC

Silva, A

Vaz, S

Teixeira, R

Neves, M

Lucio, P

Joao, C

Costa, DC

Document Type

Article in Press

Year published

2023

Published

in JOURNAL OF DIGITAL IMAGING, ISSN: 0897-1889

Volume: 36, Issue: 4, Pages: 1864-1876 (13)

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Scopus^®

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Pubmed^®

Google Scholar^®

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Publication Identifiers

DOI: 10.1007/s10278-023-00823-y

Pubmed: 37059891

Scopus: 2-s2.0-85152681187

Wos: WOS:000968792200004

Source Identifiers

ISSN: 0897-1889

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Evaluation of Semiautomatic and Deep Learning-Based Fully Automatic Segmentation Methods on [F-18]Fdg Pet/Ct Images from Patients with Lymphoma: Influence on Tumor Characterization

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Evaluation of Semiautomatic and Deep Learning-Based Fully Automatic Segmentation Methods on [F-18]Fdg Pet/Ct Images from Patients with Lymphoma: Influence on Tumor Characterization

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