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Book Cover
E-book
Author 3D Head and Neck Tumor Segmentation in PET/CT Challenge (1st : 2020 : Online)

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.)
Published Cham, Switzerland : Springer, [2021]

Copies

Description 1 online resource (x, 109 pages) : illustrations (some color)
Series Lecture notes in computer science ; 12603
LNCS sublibrary, SL 6, Image processing, computer vision, pattern recognition, and graphics
Lecture notes in computer science ; 12603.
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.
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 & 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
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 Includes bibliographical references and index
Notes Online resource; title from PDF title page (SpringerLink, viewed March 3, 2021)
Subject Diagnostic imaging -- Data processing -- Congresses
Artificial intelligence -- Medical applications -- Congresses
Cancer -- Treatment -- Technological innovations -- Congresses
Optical data processing.
Bioinformatics.
Machine learning.
Software engineering.
Computational Biology
Machine Learning
Artificial intelligence -- Medical applications
Bioinformatics
Cancer -- Treatment -- Technological innovations
Diagnostic imaging -- Data processing
Machine learning
Optical data processing
Software engineering
Genre/Form proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Form Electronic book
Author 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.
ISBN 9783030671945
3030671941
Other Titles HECKTOR 2020
MICCAI 2020