Head and Neck Tumor Segmentation
First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings
Editors: Andrearczyk, Vincent, Oreiller, Valentin, Depeursinge, Adrien (Eds.)
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- About this book
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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.
- Table of contents (11 chapters)
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Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT
Pages 1-21
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Two-Stage Approach for Segmenting Gross Tumor Volume in Head and Neck Cancer with CT and PET Imaging
Pages 22-27
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The Head and Neck Tumor Segmentation Using nnU-Net with Spatial and Channel ‘Squeeze & Excitation’ Blocks
Pages 28-36
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Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images
Pages 37-43
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Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network
Pages 44-52
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Table of contents (11 chapters)
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Bibliographic Information
- 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
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- Vincent Andrearczyk
- Valentin Oreiller
- Adrien Depeursinge
- Series Title
- Image Processing, Computer Vision, Pattern Recognition, and Graphics
- Series Volume
- 12603
- Copyright
- 2021
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer Nature Switzerland AG
- eBook ISBN
- 978-3-030-67194-5
- DOI
- 10.1007/978-3-030-67194-5
- Softcover ISBN
- 978-3-030-67193-8
- Edition Number
- 1
- Number of Pages
- X, 109
- Number of Illustrations
- 3 b/w illustrations, 29 illustrations in colour
- Topics
