Artificial intelligence in lymphoma PET imaging: a scoping review (current trends and future directions)
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Toward high-throughput artificial intelligence-based segmentation in oncological PET imaging
An array of artificial intelligence (AI) techniques in the field of medical imaging has emerged
in the past decade for automated image segmentation. 1 Medical image segmentation seeks …
in the past decade for automated image segmentation. 1 Medical image segmentation seeks …
Lymphoma segmentation from 3D PET-CT images using a deep evidential network
An automatic evidential segmentation method based on Dempster-Shafer theory and deep
learning is proposed to segment lymphomas from three-dimensional Positron Emission …
learning is proposed to segment lymphomas from three-dimensional Positron Emission …
Convolutional neural networks for automated PET/CT detection of diseased lymph node burden in patients with lymphoma
AJ Weisman, MW Kieler, SB Perlman… - Radiology: Artificial …, 2020 - pubs.rsna.org
Purpose To automatically detect lymph nodes involved in lymphoma on fluorine 18 (18F)
fluorodeoxyglucose (FDG) PET/CT images using convolutional neural networks (CNNs) …
fluorodeoxyglucose (FDG) PET/CT images using convolutional neural networks (CNNs) …
TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images—a multi-center generalizability analysis
Purpose Total metabolic tumor volume (TMTV) segmentation has significant value enabling
quantitative imaging biomarkers for lymphoma management. In this work, we tackle the …
quantitative imaging biomarkers for lymphoma management. In this work, we tackle the …
[HTML][HTML] Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients
AJ Weisman, J Kim, I Lee, KM McCarten, S Kessel… - EJNMMI physics, 2020 - Springer
Purpose For pediatric lymphoma, quantitative FDG PET/CT imaging features such as
metabolic tumor volume (MTV) are important for prognosis and risk stratification strategies …
metabolic tumor volume (MTV) are important for prognosis and risk stratification strategies …
[HTML][HTML] Evaluation of an automatic classification algorithm using convolutional neural networks in oncological positron emission tomography
P Pinochet, F Eude, S Becker, V Shah, L Sibille… - Frontiers in …, 2021 - frontiersin.org
Introduction: Our aim was to evaluate the performance in clinical research and in clinical
routine of a research prototype, called positron emission tomography (PET) Assisted …
routine of a research prototype, called positron emission tomography (PET) Assisted …
Development and validation of an 18F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extranodal natural killer/T cell lymphoma
H Wang, S Zhao, L Li, R Tian - European Radiology, 2020 - Springer
Objectives To identify an 18 F-fluorodeoxyglucose (18 F-FDG) positron emission
tomography (PET) radiomics-based model for predicting progression-free survival (PFS) and …
tomography (PET) radiomics-based model for predicting progression-free survival (PFS) and …
HD-RDS-UNet: Leveraging spatial-temporal correlation between the decoder feature maps for lymphoma segmentation
M Wang, H Jiang, T Shi, Y Yao - IEEE Journal of Biomedical …, 2021 - ieeexplore.ieee.org
Lymphoma is cancer originated in the lymphatic system. Clinically, automatic and accurate
lymphoma segmentation is critical yet challenging. Recently, UNet-like architectures are …
lymphoma segmentation is critical yet challenging. Recently, UNet-like architectures are …
PSR-Nets: Deep neural networks with prior shift regularization for PET/CT based automatic, accurate, and calibrated whole-body lymphoma segmentation
M Wang, H Jiang, T Shi, Z Wang, J Guo, G Lu… - Computers in Biology …, 2022 - Elsevier
Lymphoma is a type of lymphatic tissue originated cancer. Automatic and accurate
lymphoma segmentation is critical for its diagnosis and prognosis yet challenging due to the …
lymphoma segmentation is critical for its diagnosis and prognosis yet challenging due to the …