Head and Neck Tumor Segmentation and Outcome Prediction : Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings / edited by Vincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge.

Format:

Book

Contributor:

Andrearczyk, Vincent, Editor.

Oreiller, Valentin, Editor.

Hatt, Mathieu, Editor.

Depeursinge, Adrien, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Lecture notes in computer science 1611-3349 ; 13209

Lecture Notes in Computer Science, 1611-3349 ; 13209

Language:

English

Subjects (All):

Image processing-Digital techniques.

Computer vision.

Education-Data processing.

Application software.

Machine learning.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computers and Education.

Computer and Information Systems Applications.

Machine Learning.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computers and Education.

Computer and Information Systems Applications.

Machine Learning.

Physical Description:

1 online resource (X, 328 pages) : 102 illustrations, 88 illustrations in color.

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

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. .

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-98253-9

9783030982539

Access Restriction:

Restricted for use by site license.