(Ebook PDF) Auto Segmentation for Radiation Oncology State of the Art 1st edition by Jinzhong Yang, Gregory Sharp, Mark Gooding 1000376346 9781000376340 full chapters

Auto-Segmentation for Radiation Oncology; State of the Art 1st edition by Jinzhong Yang, Gregory C. Sharp, Mark J. Gooding  – Ebook PDF Instant Download/DeliveryISBN: 1000376346, 9781000376340

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Product details:

ISBN-10 ‏ : ‎1000376346 

ISBN-13 ‏ : ‎ 9781000376340

Author :  Jinzhong Yang, Gregory C. Sharp, Mark J. Gooding 

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

Auto-Segmentation for Radiation Oncology; State of the Art 1st Table of contents:

Chapter 1 Introduction to Auto-Segmentation in Radiation Oncology
PART I Multi-Atlas for Auto-Segmentation
Chapter 2 Introduction to Multi-Atlas Auto-Segmentation
Chapter 3 Evaluation of Atlas Selection: How Close Are We to Optimal Selection?
Chapter 4 Deformable Registration Choices for Multi-Atlas Segmentation
Chapter 5 Evaluation of a Multi-Atlas Segmentation System
PART II Deep Learning for Auto-Segmentation
Chapter 6 Introduction to Deep Learning-Based Auto-Contouring for Radiotherapy
Chapter 7 Deep Learning Architecture Design for Multi-Organ Segmentation
Chapter 8 Comparison of 2D and 3D U-Nets for Organ Segmentation
Chapter 9 Organ-Specific Segmentation Versus Multi-Class Segmentation Using U-Net
Chapter 10 Effect of Loss Functions in Deep Learning-Based Segmentation
Chapter 11 Data Augmentation for Training Deep Neural Networks
Chapter 12 Identifying Possible Scenarios Where a Deep Learning Auto-Segmentation Model Could Fail
PART III Clinical Implementation Concerns
Chapter 13 Clinical Commissioning Guidelines
Chapter 14 Data Curation Challenges for Artificial Intelligence
Chapter 15 On the Evaluation of Auto-Contouring in Radiotherapy

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