Auto-Segmentation for Radiation Oncology
State of the Art
- Available for pre-order. Item will ship after April 19, 2021
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.
- 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
Table of Contents
Chapter 1. Introduction of auto-segmentation in radiation oncology.
Jinzhong Yang, Gregory C. Sharp, Mark J. Gooding
Chapter 2.Introduction to multi-atlas auto-segmentation.
Gregory C. Sharp
Chapter 3. Evaluation of atlas selection: How close are we to optimal?
Mark J. Gooding
Chapter 4. Deformable registration choices for multi-atlas segmentation.
Keyur Shah, James Shackleford, Nagarajan Kandasamy, Gregory C. Sharp
Chapter 5. Evaluation of a multi-atlas segmentation system.
Raymond Fang, Laurence Court, and Jinzhong Yang
Chapter 6. Introduction to deep learning-based auto-contouring for radiotherapy.
Mark J. Gooding
Chapter 7. Deep Learning Architecture Design for Multi-organ Segmentation.
Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu and Xiaofeng Yang
Chapter 8. Comparison of 2D and 3D UNets for Organ Segmentation.
Dongdong Gu, Zhong Xue
Chapter 9. Organ specific segmentation versus multi-class segmentation using UNet.
Xue Feng, Quan Chen
Chapter 10. Effect of loss functions in deep learning-based segmentation.
Evan Porter, David Solis, Payton Bruckmeier, Zaid A. Siddiqui, Leonid Zamdborg, Thomas Guerrero
Chapter 11. Data augmentation for training deep neural networks .
Zhao Peng, Jieping Zhou, Xi Fang, Pingkun Yan, Hongming Shan, Ge Wang, X. George Xu, Xi Pei
Chapter 12. Identifying possible scenarios where a deep learning auto-segmentation model could fail.
Carlos E. Cardenas
Chapter 13. Clinical commissioning guidelines.
Chapter 14. Data curation challenges for artificial intelligence.
Ken Chang, Mishka Gidwani, Jay B. Patel, Matthew D. Li, Jayashree Kalpathy-Cramer
Chapter 15. On the evaluation of auto-contouring in radiotherapy
Mark J. Gooding
Jinzhong Yang received his B.S. and M.S. degrees in Electrical Engineering from University of Science and Technology of China, China, in 1998 and 2001 and his Ph.D. degree in Electrical Engineering from Lehigh University in 2006. In July 2008, Dr. Yang joined the University of Texas MD Anderson Cancer Center as a Sr. Computational Scientist, and since January 2015 he is an Assistant Professor of Radiation Physics. Dr. Yang is a board-certified medical physicist. His research interest focuses on deformable image registration and image segmentation for radiation treatment planning and image-guided adaptive radiotherapy, radiomics for radiation treatment outcome modeling and prediction, and novel imaging methodologies and applications in radiotherapy. Greg Sharp received a PhD in Computer Science and Engineering from the University of Michigan, and is currently Associate Professor in Radiation Oncology at Massachusetts General Hospital and Harvard Medical School. His primary research interests are in medical image processing and image-guided radiation therapy, where he is active in the open source software community. Mark Gooding obtained his MEng in Engineering science in 2000 and DPhil in Medical Imaging in 2004, both from the University of Oxford. He was employed as a postdoctoral researcher both in university and NHS settings, where his focus was largely around the use of 3D ultrasound segmentation in women’s health. In 2009, he joined Mirada Medical, motivated by a desire to see technical innovation translated into clinical practice. While there, he has worked on a broad spectrum of clinical applications, developing algorithms and products for both diagnostic and therapeutic purposes. If given a free choice of research topic, his passion is for improving image segmentation, but in practice he is keen to address any technical challenge. Dr Gooding now leads the research team at Mirada, where in addition to the commercial work he continues to collaborate both clinically and academically.