Joint low-count PET/CT segmentation and reconstruction with paired variational neural networks

2020 ◽  
Author(s):  
Hongki Lim ◽  
Yuni K. Dewaraja ◽  
Jeffrey A. Fessler
Keyword(s):  
Neural Networks ◽  
Pet Ct ◽  
Ct Segmentation

Investigating the contribution of pre- and per-treatment 18F-FDG PET-CT segmentation methodologies for post-treatment tumor recurrence prediction in cervical cancer

IRBM ◽  
2013 ◽  
Vol 34 (4-5) ◽  
pp. 274-277 ◽  
Author(s):  
G. Roman-Jimenez ◽  
J.-D. Ospina ◽  
J. Leseur ◽  
A. Devillers ◽  
J. Castelli ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Fdg Pet ◽  
Tumor Recurrence ◽  
Post Treatment ◽  
Recurrence Prediction ◽  
Pet Ct ◽  
18F Fdg ◽  
Ct Segmentation ◽  
Fdg Pet Ct

scholarly journals 18F-FDG PET/CT Uptake Classification in Lymphoma and Lung Cancer by Using Deep Convolutional Neural Networks

Radiology ◽  
2020 ◽  
Vol 294 (2) ◽  
pp. 445-452 ◽  
Author(s):  
Ludovic Sibille ◽  
Robert Seifert ◽  
Nemanja Avramovic ◽  
Thomas Vehren ◽  
Bruce Spottiswoode ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fdg Pet ◽  
Deep Convolutional Neural Networks ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

scholarly journals An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Mariana A. Nogueira ◽  
Pedro H. Abreu ◽  
Pedro Martins ◽  
Penousal Machado ◽  
Hugo Duarte ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Treatment Response ◽  
Ct Images ◽  
Oncological Patients ◽  
Pet Ct ◽  
Artificial Neural

scholarly journals Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks

2021 ◽  
Author(s):  
Junyu Chen ◽  
Ye Li ◽  
Licia P. Luna ◽  
Hyun Woo Chung ◽  
Steven P. Rowe ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fuzzy Clustering ◽  
Ct Segmentation

Attention-enabled 3D boosted convolutional neural networks for semantic CT segmentation using deep supervision

2019 ◽  
Vol 64 (13) ◽  
pp. 135001 ◽  
Author(s):  
Vasant Kearney ◽  
Jason W Chan ◽  
Tianqi Wang ◽  
Alan Perry ◽  
Sue S Yom ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Ct Segmentation

Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks – Initial results

Lung Cancer ◽  
2018 ◽  
Vol 126 ◽  
pp. 170-173 ◽  
Author(s):  
Moritz Schwyzer ◽  
Daniela A. Ferraro ◽  
Urs J. Muehlematter ◽  
Alessandra Curioni-Fontecedro ◽  
Martin W. Huellner ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Automated Detection ◽  
Ultralow Dose ◽  
Pet Ct ◽  
Initial Results

scholarly journals Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma

2020 ◽  
Vol 2 (5) ◽  
pp. e200016 ◽  
Author(s):  
Amy J. Weisman ◽  
Minnie W. Kieler ◽  
Scott B. Perlman ◽  
Martin Hutchings ◽  
Robert Jeraj ◽  
...  
Keyword(s):  
Neural Networks ◽  
Lymph Node ◽  
Convolutional Neural Networks ◽  
Pet Ct

scholarly journals Risk Stratification Using 18F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1581
Author(s):  
Sebastian Marschner ◽  
Elia Lombardo ◽  
Lena Minibek ◽  
Adrien Holzgreve ◽  
Lena Kaiser ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Artificial Neural Networks ◽  
Fdg Pet ◽  
Feature Sets ◽  
Clinical Variables ◽  
Pet Ct ◽  
18F Fdg ◽  
Artificial Neural ◽  
Fdg Pet Ct

This study retrospectively analyzed the performance of artificial neural networks (ANN) to predict overall survival (OS) or locoregional failure (LRF) in HNSCC patients undergoing radiotherapy, based on 2-[18F]FDG PET/CT and clinical covariates. We compared predictions relying on three different sets of features, extracted from 230 patients. Specifically, (i) an automated feature selection method independent of expert rating was compared with (ii) clinical variables with proven influence on OS or LRF and (iii) clinical data plus expert-selected SUV metrics. The three sets were given as input to an artificial neural network for outcome prediction, evaluated by Harrell’s concordance index (HCI) and by testing stratification capability. For OS and LRF, the best performance was achieved with expert-based PET-features (0.71 HCI) and clinical variables (0.70 HCI), respectively. For OS stratification, all three feature sets were significant, whereas for LRF only expert-based PET-features successfully classified low vs. high-risk patients. Based on 2-[18F]FDG PET/CT features, stratification into risk groups using ANN for OS and LRF is possible. Differences in the results for different feature sets confirm the relevance of feature selection, and the key importance of expert knowledge vs. automated selection.

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