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"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)
Table of Contents
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
