Overview
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12603)
Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)
Included in the following conference series:
Conference proceedings info: HECKTOR 2020.
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About this book
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.
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Keywords
- artificial intelligence
- automatic segmentations
- bioinformatics
- computer vision
- computerized tomography
- ct image
- image analysis
- image processing
- image segmentation
- machine learning
- medical images
- neural networks
- object recognition
- object segmentation
- pattern recognition
- segmentation methods
- software design
- software engineering
Table of contents (11 papers)
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Head and Neck Tumor Segmentation
Editors and Affiliations
Bibliographic Information
Book Title: Head and Neck Tumor Segmentation
Book Subtitle: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings
Editors: Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-030-67194-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Softcover ISBN: 978-3-030-67193-8Published: 13 January 2021
eBook ISBN: 978-3-030-67194-5Published: 12 January 2021
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: X, 109
Number of Illustrations: 3 b/w illustrations, 29 illustrations in colour
Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Computational Biology/Bioinformatics, Machine Learning, Software Engineering