1st Edition

Auto-Segmentation for Radiation Oncology State of the Art

Edited By Jinzhong Yang, Gregory C. Sharp, Mark J. Gooding Copyright 2021
    274 Pages 101 B/W Illustrations
    by CRC Press

    274 Pages 101 B/W Illustrations
    by CRC Press

    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

    Contents

    Foreword I..........................................................................................................................................ix

    Foreword II........................................................................................................................................xi

    Editors............................................................................................................................................. xiii

    Contributors......................................................................................................................................xv

    Chapter 1 Introduction to Auto-Segmentation in Radiation Oncology.........................................1

    Jinzhong Yang, Gregory C. Sharp, and Mark J. Gooding

    Part I Multi-Atlas for Auto-Segmentation

    Chapter 2 Introduction to Multi-Atlas Auto-Segmentation......................................................... 13

    Gregory C. Sharp

    Chapter 3 Evaluation of Atlas Selection: How Close Are We to Optimal Selection?................. 19

    Mark J. Gooding

    Chapter 4 Deformable Registration Choices for Multi-Atlas Segmentation............................... 39

    Keyur Shah, James Shackleford, Nagarajan Kandasamy, and Gregory C. Sharp

    Chapter 5 Evaluation of a Multi-Atlas Segmentation System......................................................49

    Raymond Fang, Laurence Court, and Jinzhong Yang

    Part II Deep Learning for Auto-Segmentation

    Chapter 6 Introduction to Deep Learning-Based Auto-Contouring for Radiotherapy................ 71

    Mark J. Gooding

    Chapter 7 Deep Learning Architecture Design for Multi-Organ Segmentation......................... 81

    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 U-Nets for Organ Segmentation.................................... 113

    Dongdong Gu and Zhong Xue

    Chapter 9 Organ-Specific Segmentation Versus Multi-Class Segmentation Using U-Net....... 125

    Xue Feng and Quan Chen

    Chapter 10 Effect of Loss Functions in Deep Learning-Based Segmentation............................ 133

    Evan Porter, David Solis, Payton Bruckmeier, Zaid A. Siddiqui,

    Leonid Zamdborg, and Thomas Guerrero

    Chapter 11 Data Augmentation for Training Deep Neural Networks ........................................ 151

    Zhao Peng, Jieping Zhou, Xi Fang, Pingkun Yan, Hongming Shan, Ge Wang,

    X. George Xu, and Xi Pei

    Chapter 12 Identifying Possible Scenarios Where a Deep Learning Auto-Segmentation

    Model Could Fail...................................................................................................... 165

    Carlos E. Cardenas

    Part III Clinical Implementation Concerns

    Chapter 13 Clinical Commissioning Guidelines......................................................................... 189

    Harini Veeraraghavan

    Chapter 14 Data Curation Challenges for Artificial Intelligence................................................ 201

    Ken Chang, Mishka Gidwani, Jay B. Patel, Matthew D. Li, and

    Jayashree Kalpathy-Cramer

    Chapter 15 On the Evaluation of Auto-Contouring in Radiotherapy.......................................... 217

    Mark J. Gooding

    Index............................................................................................................................................... 253

    Biography

    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)