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

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 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