Auto-Segmentation for Radiation Oncology: State of the Art — 1st Edition (2021)
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Description
Auto-Segmentation for Radiation Oncology: State of the Art
📘 1st Edition · 2021
Study automated contouring in radiation oncology through a reference covering multi-atlas methods, deep learning, evaluation, and implementation. The text connects algorithmic design with the considerations involved in using auto-segmentation systems.
📚 Inside this eBook
- Multi-atlas segmentation and atlas selection
- Deformable registration and system evaluation
- Deep-learning architectures, U-Nets, and multi-organ segmentation
- Loss functions, data augmentation, and potential failure scenarios
- Clinical commissioning, data curation, and auto-contouring evaluation
🔎 Why add it to your reading list?
The progression from methods to implementation supports a coherent technical study plan. Readers can compare approaches while considering how performance and failure modes are evaluated.
👩⚕️ Who is it for?
Medical physicists, radiation oncology researchers, clinicians working with contouring systems, and biomedical AI specialists.
📖 Book details
- Authors / editors: Jinzhong Yang, Gregory C. Sharp, and Mark J. Gooding, editors
- Edition: 1st Edition
- Year of supplied edition: 2021
- Publisher: CRC Press
- ISBN: 9780429323782
- Format: English-language PDF eBook
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