Overview
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12587)
Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)
Included in the following conference series:
Conference proceedings info: MICCAI 2020.
Access this book
Tax calculation will be finalised at checkout
Other ways to access
About this book
The 19 papers presented in this volume were carefully reviewed and selected form numerous submissions. The ABCs challenge aims to identify the best methods of segmenting brain structures that serve as barriers to the spread of brain cancers and structures to be spared from irradiation, for use in computer assisted target definition for glioma and radiotherapy plan optimization. The papers of the L2R challenge cover a wide spectrum of conventional and learning-based registration methods and often describe novel contributions. The main goal of the TN-SCUI challenge is tofind automatic algorithms to accurately segment and classify the thyroid nodules in ultrasound images.
*The challenges took place virtually due to the COVID-19 pandemic.
Similar content being viewed by others
Keywords
- artificial intelligence
- automatic segmentations
- bioinformatics
- classification methods
- computer vision
- deep learning
- image enhancement
- image processing
- image reconstruction
- image registration
- image segmentation
- machine learning
- medical images
- neural networks
- neuroimaging
- pattern recognition
- segmentation methods
- ultrasound images
Table of contents (19 papers)
-
ABCs – Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images
-
L2R – Learn2Reg: Multitask and Multimodal 3D Medical Image Registration
-
TN-SCUI – Thyroid Nodule Segmentation and Classification in Ultrasound Images
Other volumes
-
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
-
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
-
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
-
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
-
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
-
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
-
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
-
Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data
Editors and Affiliations
Bibliographic Information
Book Title: Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data
Book Subtitle: MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings
Editors: Nadya Shusharina, Mattias P. Heinrich, Ruobing Huang
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-030-71827-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Softcover ISBN: 978-3-030-71826-8Published: 13 March 2021
eBook ISBN: 978-3-030-71827-5Published: 12 March 2021
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XIX, 156
Number of Illustrations: 3 b/w illustrations, 54 illustrations in colour
Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Artificial Intelligence, Computational Biology/Bioinformatics