Auto-Segmentation for Radiation Oncology

State of the Art
Herausgeber: Yang, Jinzhong; Gooding, Mark J; Sharp, Gregory C

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 256
• Erscheinungstermin: 19. April 2021
• Englisch
• Abmessung: 254mm x 178mm x 18mm
• Gewicht: 699g
• ISBN-13: 9780367336004
• ISBN-10: 0367336006
• Artikelnr.: 61605528

Autorenporträt

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.

Inhaltsangabe

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