Head and Neck Tumor Segmentation : First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings / edited by Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge.

Format:

Book

Contributor:

Andrearczyk, Vincent, Editor.

Oreiller, Valentin, Editor.

Depeursinge, Adrien, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12603

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12603

Language:

English

Subjects (All):

Image processing-Digital techniques.

Computer vision.

Bioinformatics.

Machine learning.

Software engineering.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computational and Systems Biology.

Machine Learning.

Software Engineering.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computational and Systems Biology.

Machine Learning.

Software Engineering.

Physical Description:

1 online resource (X, 109 pages) : 32 illustrations, 29 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

This book constitutes the First 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 pandemic. The 2 full and 8 short papers presented together with an overview paper in this volume 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 204 delineated PET/CT images was made available for training as well as 53 PET/CT images for testing. Various deep learning methods were developed by the participants with excellent results.

Contents:

Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

Two-stage approach for segmenting gross tumor volume in head and neck cancer with CT and PET imaging

The Head and Neck Tumor Segmentation Using nnU-Net with Spatial and Channel 'Squeeze and Excitation' Blocks

Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images

Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network

Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT images

Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images

Oropharyngeal Tumour Segmentation using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge

Patch-based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated Convolutions

Tumor Segmentation in Patients with Head and Neck Cancers using Deep Learning based-on Multi-modality PET/CT Images

GAN-based Bi-modal Segmentation using Mumford-Shah Loss: Application to Head and Neck Tumors in PET-CT Images.

Other Format:

Printed edition:

ISBN:

978-3-030-67194-5

9783030671945

Access Restriction:

Restricted for use by site license.