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Full 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
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
