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 : 2021-04-19
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
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
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.
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
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.
Quality And Safety In Radiation Oncology
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Author : Adam P. Dicker, MD, PhD
language : en
Publisher: Springer Publishing Company
Release Date : 2016-08-17
Quality And Safety In Radiation Oncology written by Adam P. Dicker, MD, PhD and has been published by Springer Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-17 with Medical categories.
Quality and Safety in Radiation Oncology is the first book to provide an authoritative and evidence-based guide to the understanding and implementation of quality and safety procedures in radiation oncology practice. Alongside the rapid growth of technology and radiotherapy treatment options for cancer in recent years, quality and safety standards are not only of the utmost importance but best practices ensuring quality and safety are crucial aspect of modern radiation oncology training. A detailed exploration and review of these standards is a necessary part of radiation oncologist’s professional competency, both in the clinical setting and at the study table while preparing for board review and MOC exams. Chapter topics range from fundamental concepts of value and quality to commissioning technology and the use of metrics. They include perspectives on quality and safety from the patient, third-party payers, as well as from the federal government. Other chapters cover prospective testing of quality, training and education, error identification and analysis, incidence reporting, as well as special technology and procedures, including MRI-guided radiation therapy, proton therapy and stereotactic body radiation therapy (SBRT), quality and safety procedures in resource-limited environments, and more. State-of-the-art quality assurance procedures and safety guidelines are the backbone of this unique and essential volume. Physicians, medical physicists, dosimetrists, radiotherapists, hospital administrators, and other healthcare professionals will find this resource an invaluable compendium of best practices in radiation oncology. Key Features: Case examples illustrate best practices and pitfalls Several dozen graphs, tables and figures help quantify the discussion of quality and safety throughout the text Section II covers all aspects of quality assurance procedures for the physicist
Machine Learning In Radiation Oncology
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Author : Wei Zhao
language : en
Publisher: Frontiers Media SA
Release Date : 2023-04-05
Machine Learning In Radiation Oncology written by Wei Zhao 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 2023-04-05 with Medical categories.
Artificial Intelligence In Radiation Oncology
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Author : Seong K Mun
language : en
Publisher: World Scientific
Release Date : 2022-12-27
Artificial Intelligence In Radiation Oncology written by Seong K Mun and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-27 with Science categories.
The clinical use of Artificial Intelligence (AI) in radiation oncology is in its infancy. However, it is certain that AI is capable of making radiation oncology more precise and personalized with improved outcomes. Radiation oncology deploys an array of state-of-the-art technologies for imaging, treatment, planning, simulation, targeting, and quality assurance while managing the massive amount of data involving therapists, dosimetrists, physicists, nurses, technologists, and managers. AI consists of many powerful tools which can process a huge amount of inter-related data to improve accuracy, productivity, and automation in complex operations such as radiation oncology.This book offers an array of AI scientific concepts, and AI technology tools with selected examples of current applications to serve as a one-stop AI resource for the radiation oncology community. The clinical adoption, beyond research, will require ethical considerations and a framework for an overall assessment of AI as a set of powerful tools.30 renowned experts contributed to sixteen chapters organized into six sections: Define the Future, Strategy, AI Tools, AI Applications, and Assessment and Outcomes. The future is defined from a clinical and a technical perspective and the strategy discusses lessons learned from radiology experience in AI and the role of open access data to enhance the performance of AI tools. The AI tools include radiomics, segmentation, knowledge representation, and natural language processing. The AI applications discuss knowledge-based treatment planning and automation, AI-based treatment planning, prediction of radiotherapy toxicity, radiomics in cancer prognostication and treatment response, and the use of AI for mitigation of error propagation. The sixth section elucidates two critical issues in the clinical adoption: ethical issues and the evaluation of AI as a transformative technology.
Machine Learning With Radiation Oncology Big Data
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Author : Jun Deng
language : en
Publisher: Frontiers Media SA
Release Date : 2019-01-21
Machine Learning With Radiation Oncology Big Data written by Jun Deng 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 2019-01-21 with categories.
Artificial Intelligence
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Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2019-07-31
Artificial Intelligence written by and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-31 with Medical categories.
Artificial intelligence (AI) is taking on an increasingly important role in our society today. In the early days, machines fulfilled only manual activities. Nowadays, these machines extend their capabilities to cognitive tasks as well. And now AI is poised to make a huge contribution to medical and biological applications. From medical equipment to diagnosing and predicting disease to image and video processing, among others, AI has proven to be an area with great potential. The ability of AI to make informed decisions, learn and perceive the environment, and predict certain behavior, among its many other skills, makes this application of paramount importance in today's world. This book discusses and examines AI applications in medicine and biology as well as challenges and opportunities in this fascinating area.