Head and Neck Tumor Segmentation and Outcome Prediction

Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings
1. Auflage 2022
ISBN: 978-3-030-98252-2
Verlag: Springer International Publishing

Buch, Englisch, Band 13209, 328 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 517 g

Reihe: Lecture Notes in Computer Science

This book constitutes the Second 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually on September 27, 2021, due to the COVID-19 pandemic.

The 29 contributions presented, as well as an overview paper, were carefully reviewed and selected form numerous 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 325 delineated  PET/CT images was made available for training. 

Zielgruppe

Research

Autoren/Hrsg.

Andrearczyk, Vincent

Depeursinge, Adrien

Oreiller, Valentin

Hatt, Mathieu

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung

Inhaltsverzeichnis

Overview of the HECKTOR Challenge at MICCAI 2021: Automatic.- Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images.- CCUT-Net: Pixel-wise Global Context Channel Attention UT-Net for head and neck tumor segmentation.- A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET Images.- Automatic Segmentation of Head and Neck (H&N) Primary Tumors in PET and CT images using 3D-Inception-ResNet Model.- The Head and Neck Tumor Segmentation in PET/CT Based on Multi-channel Attention Network.- Multimodal Spatial Attention Network for Automatic Head and Neck Tumor Segmentation in FDG-PET and CT Images.- PET Normalizations to Improve Deep Learning Auto-Segmentation of Head and Neck Tumors in 3D PET/CT.- The Head and Neck Tumor Segmentation based on 3D U-Net: 3D U-net applied to Simple Attention Module for Head and Neck tumor segmentation in PET and CT images.- Skip-SCSE Multi-Scale Attention and Co-Learning method for Oropharyngeal Tumor Segmentation on multi-modal PET-CT images.- Head and Neck Cancer Primary Tumor Auto Segmentation using Model Ensembling of Deep Learning in PET/CT Images.- Priori and Posteriori Attention for Generalizing Head and Neck Tumors Segmentation.- Head and Neck Tumor Segmentation with Deeply-Supervised 3D UNet and Progression-Free Survival Prediction with Linear Model.- Deep learning based GTV delineation and progression free survival risk score prediction for head and neck cancer patients.- Multi-task Deep Learning for Joint Tumor Segmentation and Outcome Prediction in Head and Neck Cancer.- PET/CT Head and Neck tumor segmentation and Progression Free Survival prediction using Deep and Machine learning techniques.- Automatic Head and Neck Tumor Segmentation and Progression Free Survival Analysis on PET/CT images.- Multimodal PET/CT Tumour Segmentation and Progression-Free Survival Prediction using a Full-scale UNet with Attention.- Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer.- Fusion-Based head and neck Tumor Segmentation and Survival prediction using Robust Deep Learning Techniques and Advanced Hybrid Machine Learning Systems.- Head and Neck Primary Tumor Segmentation using Deep Neural Networks and Adaptive Ensembling.- Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks.- Dual-Path Connected CNN for Tumor Segmentation of Combined PET-CT Images and Application to Survival Risk Prediction.- Deep Supervoxel Segmentation Survival Anaylsis in Head and Neck Cancer Patients.- A Hybrid Radiomics Approach to Modeling Progression-free Survival in Head and Neck Cancers.- An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data.- Progression Free Survival Prediction for Head and Neck Cancer using Deep Learning based on Clinical and PET/CT Imaging Data.- Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma.- Self-supervised multi-modality image feature extraction for the progression free survival prediction in head and neck cancer.- Comparing deep learning and conventional machine learning for outcome prediction of head and neck cancer in PET/CT.