Head and neck tumor segmentation : First Challenge, HECKTOR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, proceedings / Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge (eds.).
3D Head and Neck Tumor Segmentation in PET/CT Challenge (1st : 2020 : Online); Andrearczyk, Vincent, editor; Oreiller, Valentin, editor; Depeursinge, Adrien, editor; International Conference on Medical Image Computing and Computer-Assisted Intervention (23rd : 2020 : Online), jointly held conference.
2021
RC78.7.D53
Available Online

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Title Head and neck tumor segmentation : First Challenge, HECKTOR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, proceedings / Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge (eds.).
Meeting Name 3D Head and Neck Tumor Segmentation in PET/CT Challenge (1st : 2020 : Online)
ISBN 9783030671945 (electronic book)
3030671941 (electronic book)
9783030671938
3030671933
DOI https://doi.org/10.1007/978-3-030-67194-5
Published Cham, Switzerland : Springer, [2021]
Language English
Description 1 online resource (x, 109 pages) : illustrations (some color)
Item Number 10.1007/978-3-030-67194-5 doi
Call Number RC78.7.D53
Dewey Decimal Classification 616.07/54
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.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed March 3, 2021).
Added Author Andrearczyk, Vincent, editor
Oreiller, Valentin, editor
Depeursinge, Adrien, editor
Added Meeting Name International Conference on Medical Image Computing and Computer-Assisted Intervention (23rd : 2020 : Online), jointly held conference.
Series Lecture notes in computer science ; 12603.
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.
Available in Other Form Print version: 9783030671938
Linked Resources Online Access
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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 & 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.

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