Auto-Segmentation for Radiation Oncology
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"This textbook provides a comprehensive overview of multi-atlas and deep learning approaches to auto-contouring. Furthermore, key questions on clinical implementation are considered. The first introductory chapter describes the main focus of this book being the Thoracic Auto-segmentation Challenge held as an event of the 2017 Annual Meeting of the American Association of Physicists in Medicine (AAPM). Several challenge participants contributed a chapter to this book, addressing a specific strength of their segmentation algorithms. The lack of broad clinical introduction of auto-segmentation, which according to the editors is partly due to the lack of commissioning guidelines, made them dedicate the third part of the book to clinical implementation concerns. The book is written for everyone working in the field of auto-segmentation in radiotherapy. The experienced editors are from academia, clinical physics, and industry; their broad experience gives excellent perspective to this book…This book was useful toward improving my understanding of deep learning-based methods in medical image segmentation. To the best of my knowledge, this is the only textbook available on auto-segmentation dedicated to radiation oncology. Practical concerns and recommendations for implementation make this textbook a must-have for every radiation oncology department."
— Charlotte Brouwer, M.Sc. in Medical Physics (December, 2021)
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This book provides a comprehensive introduction to current state-of-the-art auto-segmentation approaches used in radiation oncology for auto-delineation of organs-of-risk for thoracic radiation treatment planning. Les mer
This book is an ideal guide for radiation oncology centers looking to learn more about potential auto-segmentation tools for their clinic in addition to medical physicists commissioning auto-segmentation for clinical use.
Features:
Up-to-date with the latest technologies in the field
Edited by leading authorities in the area, with chapter contributions from subject area specialists
All approaches presented in this book are validated using a standard benchmark dataset established by the Thoracic Auto-segmentation Challenge held as an event of the 2017 Annual Meeting of American Association of Physicists in Medicine
Detaljer
- Forlag
- CRC Press
- Innbinding
- Innbundet
- Språk
- Engelsk
- Sider
- 256
- ISBN
- 9780367336004
- Utgivelsesår
- 2021
- Format
- 25 x 18 cm
Anmeldelser
«
"This textbook provides a comprehensive overview of multi-atlas and deep learning approaches to auto-contouring. Furthermore, key questions on clinical implementation are considered. The first introductory chapter describes the main focus of this book being the Thoracic Auto-segmentation Challenge held as an event of the 2017 Annual Meeting of the American Association of Physicists in Medicine (AAPM). Several challenge participants contributed a chapter to this book, addressing a specific strength of their segmentation algorithms. The lack of broad clinical introduction of auto-segmentation, which according to the editors is partly due to the lack of commissioning guidelines, made them dedicate the third part of the book to clinical implementation concerns. The book is written for everyone working in the field of auto-segmentation in radiotherapy. The experienced editors are from academia, clinical physics, and industry; their broad experience gives excellent perspective to this book…This book was useful toward improving my understanding of deep learning-based methods in medical image segmentation. To the best of my knowledge, this is the only textbook available on auto-segmentation dedicated to radiation oncology. Practical concerns and recommendations for implementation make this textbook a must-have for every radiation oncology department."
— Charlotte Brouwer, M.Sc. in Medical Physics (December, 2021)
»