Auto Segmentation For Radiation Oncology
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Auto Segmentation For Radiation Oncology
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Author : Jinzhong Yang
language : en
Publisher: CRC Press
Release Date : 2021-04-18
Auto Segmentation For Radiation Oncology written by Jinzhong Yang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-18 with Science categories.
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
Artificial Intelligence In Radiation Oncology And Biomedical Physics
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Author : Gilmer Valdes
language : en
Publisher: CRC Press
Release Date : 2023-08-14
Artificial Intelligence In Radiation Oncology And Biomedical Physics written by Gilmer Valdes and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-14 with Computers categories.
This pioneering book explores how machine learning and other AI techniques impact millions of cancer patients who benefit from ionizing radiation. It features contributions from global researchers and clinicians, focusing on the clinical applications of machine learning for medical physics. AI and machine learning have attracted much recent attention and are being increasingly adopted in medicine, with many clinical components and commercial software including aspects of machine learning integration. General principles and important techniques in machine learning are introduced, followed by discussion of clinical applications, particularly in radiomics, outcome prediction, registration and segmentation, treatment planning, quality assurance, image processing, and clinical decision-making. Finally, a futuristic look at the role of AI in radiation oncology is provided. This book brings medical physicists and radiation oncologists up to date with the most novel applications of machine learning to medical physics. Practitioners will appreciate the insightful discussions and detailed descriptions in each chapter. Its emphasis on clinical applications reaches a wide audience within the medical physics profession.
Machine And Deep Learning In Oncology Medical Physics And Radiology
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Author : Issam El Naqa
language : en
Publisher: Springer Nature
Release Date : 2022-02-02
Machine And Deep Learning In Oncology Medical Physics And Radiology written by Issam El Naqa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with Science categories.
This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Machine Learning Based Adaptive Radiotherapy Treatments From Bench Top To Bedside
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Author : Jiahan Zhang
language : en
Publisher: Frontiers Media SA
Release Date : 2023-05-12
Machine Learning Based Adaptive Radiotherapy Treatments From Bench Top To Bedside written by Jiahan Zhang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-12 with Medical categories.
The Benefit Of Automatic Segmentation Of Intracranial Organs At Risk For Radiation Therapy
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Author : Matthew A. Deeley
language : en
Publisher:
Release Date : 2013
The Benefit Of Automatic Segmentation Of Intracranial Organs At Risk For Radiation Therapy written by Matthew A. Deeley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Electronic dissertations categories.
Machine Learning And Artificial Intelligence In Radiation Oncology
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Author : Barry S. Rosenstein
language : en
Publisher: Academic Press
Release Date : 2023-12-02
Machine Learning And Artificial Intelligence In Radiation Oncology written by Barry S. Rosenstein and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-02 with Science categories.
Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology. Presents content written by practicing clinicians and research scientists, allowing a healthy mix of both new clinical ideas as well as perspectives on how to translate research findings into the clinic Provides perspectives from artificial intelligence (AI) industry researchers to discuss novel theoretical approaches and possibilities on academic collaborations Brings diverse points-of-view from an international group of experts to provide more balanced viewpoints on a complex topic
Machine Learning In Radiation Oncology
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Author : Issam El Naqa
language : en
Publisher: Springer
Release Date : 2015-06-19
Machine Learning In Radiation Oncology written by Issam El Naqa and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-19 with Medical categories.
This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
Automatic Segmentation Of Structures In Ct Images For Head And Neck Intensity Modulated Radiation Therapy
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Author : Antong Chen
language : en
Publisher:
Release Date : 2012
Automatic Segmentation Of Structures In Ct Images For Head And Neck Intensity Modulated Radiation Therapy written by Antong Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Electronic dissertations categories.
Auto Segmentation For Radiation Oncology
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Author : Jinzhong Yang
language : en
Publisher: CRC Press
Release Date : 2021-04-19
Auto Segmentation For Radiation Oncology written by Jinzhong Yang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-19 with Science categories.
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
Deep Learning And Data Labeling For Medical Applications
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Author : Gustavo Carneiro
language : en
Publisher: Springer
Release Date : 2016-10-07
Deep Learning And Data Labeling For Medical Applications written by Gustavo Carneiro and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-07 with Computers categories.
This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.