PERHAPS A GIFT VOUCHER FOR MUM?: MOTHER'S DAY
Jinzhong Yang earned his BS and MS degrees in Electrical Engineering from the University of Science and Technology of China, in 1998 and 2001, and his PhD 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 Senior Computational Scientist, and since January 2015 he has been 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 earned 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 earned 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 hospital settings, where his focus was largely around the use of 3D ultrasound segmentation in women’s health. In 2009, he joined Mirada Medical Ltd, 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.
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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