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. Containing the latest, cutting edge
technologies and treatments, it explores deep-learning methods,
multi-atlas-based methods, and model-based methods that are
currently being developed for clinical radiation oncology
applications. Each chapter focuses on a specific aspect of
algorithm choices and discusses the impact of the different
algorithm modules to the algorithm performance as well as the
implementation issues for clinical use (including data curation
challenges and auto-contour evaluations). 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
General
| Imprint: |
Crc Press
|
| Country of origin: |
United Kingdom |
| Series: |
Series in Medical Physics and Biomedical Engineering |
| Release date: |
May 2023 |
| First published: |
2021 |
| Editors: |
Jinzhong Yang
• Gregory C. Sharp
• Mark J. Gooding
|
| Dimensions: |
254 x 178mm (L x W) |
| Pages: |
256 |
| ISBN-13: |
978-0-367-76122-6 |
| Categories: |
Books
|
| LSN: |
0-367-76122-X |
| Barcode: |
9780367761226 |
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