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Pet/Ct Cross-Modal Automatic Segmentation-Based Prognostic Assessment of Diffuse Large B-Cell Lymphoma Patients

https://doi.org/10.2139/ssrn.4824615
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2021 Update on Diffuse large B cell lymphoma: A review of current data and potential applications on risk stratification and management
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Diffuse large B‐cell lymphoma segmentation in PET‐CT images via hybrid learning for feature fusion
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Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation
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U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/s00259-010-1688-6openalex-extracted
PET functional volume delineation: a robustness and repeatability study
inferredcites →https://doi.org/10.1007/s00259-014-2822-7openalex-extracted
Pretherapy metabolic tumour volume is an independent predictor of outcome in patients with diffuse large B-cell lymphoma
inferredcites →https://doi.org/10.1007/s00259-014-2961-xopenalex-extracted
FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0
inferredcites →https://doi.org/10.1007/s00259-019-04606-yopenalex-extracted
Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT
inferredcites →https://doi.org/10.1007/s00259-020-05080-7openalex-extracted
Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network.
inferredcites →https://doi.org/10.1007/s00330-022-08573-1openalex-extracted
Deep learning–based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images
inferredcites →https://doi.org/10.1007/s10278-023-00823-yopenalex-extracted
Evaluation of Semiautomatic and Deep Learning–Based Fully Automatic Segmentation Methods on [18F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization
inferredcites →https://doi.org/10.1007/s12350-021-02649-zopenalex-extracted
Aortic wall segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based versus manual segmentation
inferredcites →https://doi.org/10.1016/j.array.2019.100004openalex-extracted
A review: Deep learning for medical image segmentation using multi-modality fusion
inferredcites →https://doi.org/10.1016/j.cmpb.2021.106043openalex-extracted
Recurrent feature fusion learning for multi-modality pet-ct tumor segmentation
inferredcites →https://doi.org/10.1016/j.compbiomed.2023.106748openalex-extracted
AATSN: Anatomy Aware Tumor Segmentation Network for PET-CT volumes and images using a lightweight fusion-attention mechanism
inferredcites →https://doi.org/10.1016/j.ejrad.2018.10.012openalex-extracted
Optimal window settings in single-source dual-energy computed tomography of the abdomen
inferredcites →https://doi.org/10.1016/j.heliyon.2020.e05267openalex-extracted
Intensity threshold based solid tumour segmentation method for Positron Emission Tomography (PET) images: A review
inferredcites →https://doi.org/10.1038/nature14539openalex-extracted
Deep learning
inferredcites →https://doi.org/10.1093/annonc/mdw011openalex-extracted
Prognostic value of baseline total metabolic tumor volume (TMTV0) measured on FDG-PET/CT in patients with peripheral T-cell lymphoma (PTCL)
inferredcites →https://doi.org/10.1109/access.2019.2963254openalex-extracted
DenseX-Net: An End-to-End Model for Lymphoma Segmentation in Whole-Body PET/CT Images
inferredcites →https://doi.org/10.1109/ipta.2014.7001923openalex-extracted
3D automated lymphoma segmentation in PET images based on cellular automata
inferredcites →https://doi.org/10.1109/isbi.2016.7493515openalex-extracted
Fully convolutional networks for multi-modality isointense infant brain image segmentation
inferredcites →https://doi.org/10.1109/tmi.2019.2918096openalex-extracted
Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks
inferredcites →https://doi.org/10.1109/tmi.2019.2923601openalex-extracted
Co-Learning Feature Fusion Maps From PET-CT Images of Lung Cancer
inferredcites →https://doi.org/10.1109/wcsp55476.2022.10039141openalex-extracted
Hybrid Attention Fusion Segmentation Network for Diffuse Large B-cell Lymphoma in PET-CT
inferredcites →https://doi.org/10.1142/s0219467820500187openalex-extracted
Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review
inferredcites →https://doi.org/10.1182/blood-2013-09-524108openalex-extracted
An enhanced International Prognostic Index (NCCN-IPI) for patients with diffuse large B-cell lymphoma treated in the rituximab era
inferredcites →https://doi.org/10.1182/blood.2019002729openalex-extracted
International prognostic indices in diffuse large B-cell lymphoma: a comparison of IPI, R-IPI, and NCCN-IPI
inferredcites →https://doi.org/10.1371/journal.pone.0123694openalex-extracted
A New Method of Detecting Pulmonary Nodules with PET/CT Based on an Improved Watershed Algorithm
inferredcites →https://doi.org/10.21037/qims-20-1166openalex-extracted
Value of total lesion glycolysis and cell-of-origin subtypes for prognostic stratification of diffuse large B-cell lymphoma patients
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