Andrearczyk / Oreiller / Hatt Head and Neck Tumor Segmentation and Outcome Prediction

Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
1. Auflage 2023
ISBN: 978-3-031-27420-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 13626, 257 Seiten, eBook

Reihe: Lecture Notes in Computer Science

This book constitutes the Third 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, which was held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, on September 22, 2022. The 22 contributions presented, as well as an overview paper, were carefully reviewed and selected from 24 submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 883 delineated  PET/CT images was made available for training.

Zielgruppe

Research

Autoren/Hrsg.

Andrearczyk, Vincent

Oreiller, Valentin

Hatt, Mathieu

Depeursinge, Adrien

Inhaltsverzeichnis

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 nodessegmentationin 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.