Head And Neck Tumor Segmentation
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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.
Head And Neck Tumor Segmentation And Outcome Prediction
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Author : Vincent Andrearczyk
language : en
Publisher: Springer Nature
Release Date : 2022-03-12
Head And Neck Tumor Segmentation And Outcome Prediction 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 2022-03-12 with Computers categories.
This book constitutes the Second 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually on September 27, 2021, due to the COVID-19 pandemic. The 29 contributions presented, as well as an overview paper, 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 325 delineated PET/CT images was made available for training.
Head And Neck Tumor Segmentation And Outcome Prediction
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Author : Vincent Andrearczyk
language : en
Publisher: Springer Nature
Release Date : 2023-03-17
Head And Neck Tumor Segmentation And Outcome Prediction 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 2023-03-17 with Computers categories.
This book constitutes the Third 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, which was held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, on September 22, 2022. The 22 contributions presented, as well as an overview paper, were carefully reviewed and selected from 24 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 883 delineated PET/CT images was made available for training.
Deep Learning For Head And Neck Tumor Segmentation
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Author :
language : en
Publisher:
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Deep Learning For Head And Neck Tumor Segmentation written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Convolutional Neural Networks For Head And Neck Tumor Segmentation On 7 Channel Multiparametric Mri A Leave One Out Analysis
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Author : Lars Bielak
language : en
Publisher:
Release Date : 2020
Convolutional Neural Networks For Head And Neck Tumor Segmentation On 7 Channel Multiparametric Mri A Leave One Out Analysis written by Lars Bielak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Head And Neck Tumor Segmentation
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Author : Vincent Andrearczyk
language : en
Publisher:
Release Date : 2022
Head And Neck Tumor Segmentation written by Vincent Andrearczyk and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Artificial intelligence categories.
Convolutional Neural Networks For Head And Neck Tumor Segmentation In Mri
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Author : Lars Bielak
language : en
Publisher:
Release Date : 2022*
Convolutional Neural Networks For Head And Neck Tumor Segmentation In Mri written by Lars Bielak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022* with categories.
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.
Improving Deep Neural Network Training With Batch Size And Learning Rate Optimization For Head And Neck Tumor Segmentation On 2d And 3d Medical Images
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Author : Zachariah Douglas
language : en
Publisher:
Release Date : 2022
Improving Deep Neural Network Training With Batch Size And Learning Rate Optimization For Head And Neck Tumor Segmentation On 2d And 3d Medical Images written by Zachariah Douglas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Medical imaging is a key tool used in healthcare to diagnose and prognose patients by aiding the detection of a variety of diseases and conditions. In practice, medical image screening must be performed by clinical practitioners who rely primarily on their expertise and experience for disease diagnosis. The ability of convolutional neural networks (CNNs) to extract hierarchical features and determine classifications directly from raw image data makes CNNs a potentially useful adjunct to the medical image analysis process. A common challenge in successfully implementing CNNs is optimizing hyperparameters for training. In this study, we propose a method which utilizes scheduled hyperparameters and Bayesian optimization to classify cancerous and noncancerous tissues (i.e., segmentation) from head and neck computed tomography (CT) and positron emission tomography (PET) scans. The results of this method are compared using CT imaging with and without PET imaging for 2D and 3D image segmentation models.
Head And Neck Cancer
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Author : Jacques Bernier
language : en
Publisher: Springer
Release Date : 2016-08-22
Head And Neck Cancer written by Jacques Bernier and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-22 with Medical categories.
This second edition provides a comprehensive view of consolidated and innovative concepts, in terms of both diagnosis and treatment. Written by leading international physicians and investigators, this book emphasizes the necessity of combining local and systemic treatments to achieve the objective of yielding higher cure rates and lower toxicities. Heavily updated from the previous edition, it highlights new surgery and radiotherapy techniques, disease awareness, patient quality of life, and comprehensive management. Head-and-neck cancers are a complex clinical entity and their response to treatment is also known to vary markedly in function of host-related factors. Notwithstanding the impressive progresses observed in the field of imaging, head and neck cancers are often diagnosed at a late stage and the presence of locally advanced disease in a significant number of patients implies the use of aggressive treatments in order to both ensure local disease control and reduce distant metastasis risks. In comparison with the first edition, Head and Neck Cancer, Second Edition provides a detailed update of innovative concepts in chemo- and bio-radiation, viral infection impact on tumor growth and response to treatment, and impact of tumor- and host-related factors on treatment outcome.