Overview
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13209)
Included in the following conference series:
Conference proceedings info: HECKTOR 2021.
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About this book
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.
Keywords
- artificial intelligence
- automatic segmentations
- classification
- computer vision
- computerized tomography
- deep learning
- education
- health informatics
- image analysis
- image processing
- image segmentation
- machine learning
- medical images
- neural networks
- pattern recognition
- performance, design, evaluation
- segmentation methods
Table of contents (30 papers)
Other volumes
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Head and Neck Tumor Segmentation and Outcome Prediction
Editors and Affiliations
Bibliographic Information
Book Title: Head and Neck Tumor Segmentation and Outcome Prediction
Book Subtitle: Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings
Editors: Vincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-030-98253-9
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2022
Softcover ISBN: 978-3-030-98252-2Published: 13 March 2022
eBook ISBN: 978-3-030-98253-9Published: 12 March 2022
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: X, 328
Number of Illustrations: 14 b/w illustrations, 88 illustrations in colour
Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Computers and Education, Computer Applications, Machine Learning