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Author:   Vincent Andrearczyk ,  Valentin Oreiller ,  Mathieu Hatt ,  Adrien Depeursinge
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2023
Volume:   13626
Weight:   0.421kg
ISBN:  

9783031274190

ISBN 10:   3031274199
Pages:   257
Publication Date:   19 March 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT 1.- Automated head and neck tumor segmentation from 3D PET/CTHECKTOR 2022 challenge report.- A Coarse-to-Fine Ensembling Framework for Head and Neck Tumorand Lymph Segmentation in CT and PET Images.- A General Web-based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT Images.- Octree Boundary Transfiner: Effcient Transformers for Tumor Segmentation Refinement.- Head and Neck Primary Tumor and Lymph Node Auto-Segmentationfor PET/CT Scans.- Fusion-based Automated Segmentation in Head and Neck Cancer via Advance Deep Learning Techniques.- Stacking Feature Maps of Multi-Scaled Medical Images in U-Net for 3DHead and Neck Tumor Segmentation.- A fine-tuned 3D U-net for primary tumor and affected lymph nodessegmentation in fused multimodal images of oropharyngeal cancer.- A U-Net convolutional neural network with multiclass Dice loss for automated segmentation of tumors and lymph nodes from head and neck cancer PET/CT images.- Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation.- Swin UNETR for tumor and lymph node delineation of multicentre oropharyngeal cancer patients with PET/CT imaging.- Simplicity is All You Need: Out-of-the-Box nnUNet followed by Binary-Weighted Radiomic Model for Segmentation and Outcome Prediction in Head and Neck PET/CT.- Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer.- Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers.- Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT images.- LC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning.- Towards Tumour Graph Learning for Survival Prediction in Head NeckCancer Patients.- Combining nnUNet and AutoML for Automatic Head and Neck Tumor Segmentation and Recurrence-Free Survival Prediction in PET/CT Images.- Head and neck cancer localization with Retina Unet for automated segmentation and time-to-event prognosis from PET/CT images.- HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG-PET/CT images.- Head and Neck Tumor Segmentation with 3D UNet and Survival Prediction with Multiple Instance Neural Network.- Deep Learning and Machine Learning Techniques for Automated PET/CT Segmentation and Survival Prediction in Head and Neck Cancer.- Deep learning and radiomics based PET/CT image feature extractionfrom auto segmented tumor volumes for recurrence-free survival prediction in oropharyngeal cancer patients.

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