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Description
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
About the Author
Jinzhong Yang earned his BS and MS degrees in Electrical Engineering from the University ofScience and Technology of China, in 1998 and 2001, and his PhD degree in Electrical Engineeringfrom Lehigh University in 2006. In July 2008, Dr Yang joined the University of Texas MD AndersonCancer Center as a Senior Computational Scientist, and since January 2015 he has been an AssistantProfessor of Radiation Physics. Dr Yang is a board-certified medical physicist. His research interestfocuses on deformable image registration and image segmentation for radiation treatment planningand image-guided adaptive radiotherapy, radiomics for radiation treatment outcome modeling andprediction, and novel imaging methodologies and applications in radiotherapy.Greg Sharp earned a PhD in Computer Science and Engineering from the University of Michiganand is currently Associate Professor in Radiation Oncology at Massachusetts General Hospitaland Harvard Medical School. His primary research interests are in medical image processing andimage-guided radiation therapy, where he is active in the open source software community.Mark Gooding earned his MEng in Engineering Science in 2000 and DPhil in Medical Imagingin 2004, both from the University of Oxford. He was employed as a postdoctoral researcher bothin university and hospital settings, where his focus was largely around the use of 3D ultrasoundsegmentation in women's health. In 2009, he joined Mirada Medical Ltd, motivated by a desire tosee technical innovation translated into clinical practice. While there, he has worked on a broadspectrum of clinical applications, developing algorithms and products for both diagnostic and therapeuticpurposes. 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 theresearch team at Mirada, where in addition to the commercial work he continues to collaborate bothclinically and academically.
