Automatic Segmentation Of Structures In Ct Images For Head And Neck Intensity Modulated Radiation Therapy
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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 : 2021-04-18
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
Automated Segmentation Of Head And Neck Cancer Using Texture Analysis With Co Registered Pet Ct Images
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Author : Huan Yu
language : en
Publisher:
Release Date : 2010
Automated Segmentation Of Head And Neck Cancer Using Texture Analysis With Co Registered Pet Ct Images written by Huan Yu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Radiation therapy is often offered as the primary treatment for head and neck cancer(HNC). Accurate target delineation is essential for the success of radiation therapy. The current target definition technique - manual delineation using Computed Tomography(CT) - is subject to high observer variability. Functional imaging modalities such as 2-[18F]-fluoro-2-deoxy-D-glucose Positron Emission Tomography(FDG-PET) can greatly improve the visualization of tumor. FDG-PET co-registered with CT has shown potential to improve the accuracy of target localization and reduce observer variability. Unfortunately, due to the limitation of PET, the degree of improvement obtained by qualitative and simple quantitative (e.g. thresholding) use of FDG-PET is not ideal. However, both PET and CT images contain a wealth of texture information that could be used to improve the accuracy of target definition. This thesis has investigated using texture analysis techniques to automatically delineate radiation targets. Firstly, PET and CT texture features with high discrimination ability were identified and a texture analysis technique- a decision tree based K Nearest Neighbour(DTKNN) classifier - was developed. DTKNN could accurately classify head and neck tissue with an area under curve(AUC) of a Receiver Operator Characteristic(ROC) of 0.95. Subsequently, an automated target delineation technique - CO-registered Multi-modality Pattern Analysis Segmentation System(COMPASS) - was developed that can delineate tumor on a voxel-by-voxel basis. COMPASS was found to accurately delineate HNC with 84% sensitivity and 95% specificity on a voxel basis per patient. To accurately evaluate the utility of the COMPASS in radiation targeting, a validation method which can combine biased observers' contours to generate a probabilistic reference for validation was developed. The method was based on maximum likelihood analysis using a simulated annealing(SA) algorithm. The results from this thesis show that texture features of both PET and CT images can enhance the discrimination between HNC and normal tissue, and an automated delineation method of HNC using texture analysis of PET and CT images can accurately and consistently define radiation targets in head and neck. This suggests that automated segmentation of radiation targets based on texture analysis techniques may significantly reduce observer variability and improve the accuracy of radiation targeting.
Landmarking And Segmentation Of 3d Ct Images
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Author : Shantanu Banik
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2009
Landmarking And Segmentation Of 3d Ct Images written by Shantanu Banik and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Medical categories.
Segmentation and landmarking of computed tomographic (CT) images of pediatric patients are important and useful in computer-aided diagnosis (CAD), treatment planning, and objective analysis of normal as well as pathological regions. Identification and segmentation of organs and tissues in the presence of tumors are difficult. Automatic segmentation of the primary tumor mass in neuroblastoma could facilitate reproducible and objective analysis of the tumor's tissue composition, shape, and size. However, due to the heterogeneous tissue composition of the neuroblastic tumor, ranging from low-attenuation necrosis to high-attenuation calcification, segmentation of the tumor mass is a challenging problem. In this context, methods are described in this book for identification and segmentation of several abdominal and thoracic landmarks to assist in the segmentation of neuroblastic tumors in pediatric CT images. Methods to identify and segment automatically the peripheral artifacts and tissues, the rib structure, the vertebral column, the spinal canal, the diaphragm, and the pelvic surface are described. Techniques are also presented to evaluate quantitatively the results of segmentation of the vertebral column, the spinal canal, the diaphragm, and the pelvic girdle by comparing with the results of independent manual segmentation performed by a radiologist. The use of the landmarks and removal of several tissues and organs are shown to assist in limiting the scope of the tumor segmentation process to the abdomen, to lead to the reduction of the false-positive error, and to improve the result of segmentation of neuroblastic tumors. Table of Contents: Introduction to Medical Image Analysis / Image Segmentation / Experimental Design and Database / Ribs, Vertebral Column, and Spinal Canal / Delineation of the Diaphragm / Delineation of the Pelvic Girdle / Application of Landmarking / Concluding Remarks
Landmarking And Segmentation Of 3d Ct Images
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Author : Shantanu Banik
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Landmarking And Segmentation Of 3d Ct Images written by Shantanu Banik and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Technology & Engineering categories.
Segmentation and landmarking of computed tomographic (CT) images of pediatric patients are important and useful in computer-aided diagnosis (CAD), treatment planning, and objective analysis of normal as well as pathological regions. Identification and segmentation of organs and tissues in the presence of tumors are difficult. Automatic segmentation of the primary tumor mass in neuroblastoma could facilitate reproducible and objective analysis of the tumor's tissue composition, shape, and size. However, due to the heterogeneous tissue composition of the neuroblastic tumor, ranging from low-attenuation necrosis to high-attenuation calcification, segmentation of the tumor mass is a challenging problem. In this context, methods are described in this book for identification and segmentation of several abdominal and thoracic landmarks to assist in the segmentation of neuroblastic tumors in pediatric CT images. Methods to identify and segment automatically the peripheral artifacts and tissues, the rib structure, the vertebral column, the spinal canal, the diaphragm, and the pelvic surface are described. Techniques are also presented to evaluate quantitatively the results of segmentation of the vertebral column, the spinal canal, the diaphragm, and the pelvic girdle by comparing with the results of independent manual segmentation performed by a radiologist. The use of the landmarks and removal of several tissues and organs are shown to assist in limiting the scope of the tumor segmentation process to the abdomen, to lead to the reduction of the false-positive error, and to improve the result of segmentation of neuroblastic tumors. Table of Contents: Introduction to Medical Image Analysis / Image Segmentation / Experimental Design and Database / Ribs, Vertebral Column, and Spinal Canal / Delineation of the Diaphragm / Delineation of the Pelvic Girdle / Application of Landmarking / Concluding Remarks
Adaptive Radiation Therapy
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Author : X. Allen Li
language : en
Publisher: CRC Press
Release Date : 2011-01-27
Adaptive Radiation Therapy written by X. Allen Li and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-27 with Medical categories.
Modern medical imaging and radiation therapy technologies are so complex and computer driven that it is difficult for physicians and technologists to know exactly what is happening at the point-of-care. Medical physicists responsible for filling this gap in knowledge must stay abreast of the latest advances at the intersection of medical imaging an
Head And Neck Tumor Segmentation
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Author : Vincent Andrearczyk
language : en
Publisher: Springer Nature
Release Date : 2021-01-12
Head And Neck Tumor Segmentation written by Vincent Andrearczyk and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-12 with Computers categories.
This book constitutes the First 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 pandemic. The 2 full and 8 short papers presented together with an overview paper in this volume were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 204 delineated PET/CT images was made available for training as well as 53 PET/CT images for testing. Various deep learning methods were developed by the participants with excellent results.
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.
Medical Image Understanding And Analysis
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Author : María Valdés Hernández
language : en
Publisher: Springer
Release Date : 2017-06-23
Medical Image Understanding And Analysis written by María Valdés Hernández and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-23 with Computers categories.
This book constitutes the refereed proceedings of the 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, held in Edinburgh, UK, in July 2017. The 82 revised full papers presented were carefully reviewed and selected from 105 submissions. The papers are organized in topical sections on retinal imaging, ultrasound imaging, cardiovascular imaging, oncology imaging, mammography image analysis, image enhancement and alignment, modeling and segmentation of preclinical, body and histological imaging, feature detection and classification. The chapters 'Model-Based Correction of Segmentation Errors in Digitised Histological Images' and 'Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering' are open access under a CC BY 4.0 license.
Applications Of Deformable Image Registration
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Author : Marc Morcos
language : en
Publisher:
Release Date : 2011
Applications Of Deformable Image Registration written by Marc Morcos and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
The contents of this thesis are best divided into two components: (i) evaluation of atlas-based segmentation and deformable contour propagation and (ii) adaptive radiation therapy using deformable electron density mapping. The first component of this thesis involves the evaluation of two commercial deformable registration systems with respect to automatic segmentation techniques. Overall, the techniques revealed that manual modifications would be required if the structures were to be used for treatment planning. The automatic segmentation methods utilized by both commercial products serve as an excellent starting point for contouring process and also reduce inter- and intra-physician variability when contouring. In the second component, we developed a framework for dose accumulation adaptive radiation therapy. By registering the planning computed tomography (CT) images to the weekly cone-beam computed tomography (CBCT) images, we were able to produce modified CBC...