Head and Neck Tumor Segmentation and Outcome Prediction

Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

AvVincent Andrearczyk,Valentin Oreiller

Häftad, Engelska, 2022

Del 13209 i serien Lecture Notes in Computer Science

789 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

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.

Produktinformation

Utgivningsdatum:2022-03-13
Mått:155 x 235 x 19 mm
Vikt:517 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:328
Förlag:Springer Nature Switzerland AG
ISBN:9783030982522

Utforska kategorier

Innehållsförteckning

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.