Automatic Tumor Segmentation With A Convolutional Neural Network In Multiparametric Mri Influence Of Distortion Correction
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Automatic Tumor Segmentation With A Convolutional Neural Network In Multiparametric Mri Influence Of Distortion Correction
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Author : Lars Bielak
language : en
Publisher:
Release Date : 2019
Automatic Tumor Segmentation With A Convolutional Neural Network In Multiparametric Mri Influence Of Distortion Correction 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 2019 with categories.
Abstract: Precise tumor segmentation is a crucial task in radiation therapy planning. Convolutional neural networks (CNNs) are among the highest scoring automatic approaches for tumor segmentation. We investigate the difference in segmentation performance of geometrically distorted and corrected diffusion-weighted data using data of patients with head and neck tumors; 18 patients with head and neck tumors underwent multiparametric magnetic resonance imaging, including T2w, T1w, T2*, perfusion (ktrans), and apparent diffusion coefficient (ADC) measurements. Owing to strong geometrical distortions in diffusion-weighted echo planar imaging in the head and neck region, ADC data were additionally distortion corrected. To investigate the influence of geometrical correction, first 14 CNNs were trained on data with geometrically corrected ADC and another 14 CNNs were trained using data without the correction on different samples of 13 patients for training and 4 patients for validation each. The different sets were each trained from scratch using randomly initialized weights, but the training data distributions were pairwise equal for corrected and uncorrected data. Segmentation performance was evaluated on the remaining 1 test-patient for each of the 14 sets. The CNN segmentation performance scored an average Dice coefficient of 0.40 +- 0.18 for data including distortion-corrected ADC and 0.37 +- 0.21 for uncorrected data. Paired t test revealed that the performance was not significantly different (P = .313). Thus, geometrical distortion on diffusion-weighted imaging data in patients with head and neck tumor does not significantly impair CNN segmentation performance in use
Brain Tumor Mri Image Segmentation Using Deep Learning Techniques
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Author : Jyotismita Chaki
language : en
Publisher: Academic Press
Release Date : 2021-11-27
Brain Tumor Mri Image Segmentation Using Deep Learning Techniques written by Jyotismita Chaki and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-27 with Science categories.
Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a description of deep learning approaches used for the segmentation of brain tumors. The book demonstrates core concepts of deep learning algorithms by using diagrams, data tables and examples to illustrate brain tumor segmentation. After introducing basic concepts of deep learning-based brain tumor segmentation, sections cover techniques for modeling, segmentation and properties. A focus is placed on the application of different types of convolutional neural networks, like single path, multi path, fully convolutional network, cascade convolutional neural networks, Long Short-Term Memory - Recurrent Neural Network and Gated Recurrent Units, and more. The book also highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in brain tumor segmentation. Provides readers with an understanding of deep learning-based approaches in the field of brain tumor segmentation, including preprocessing techniques Integrates recent advancements in the field, including the transformation of low-resolution brain tumor images into super-resolution images using deep learning-based methods, single path Convolutional Neural Network based brain tumor segmentation, and much more Includes coverage of Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN), Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN), Generative Adversarial Networks (GAN), Auto Encoder based brain tumor segmentation, and Ensemble deep learning Model based brain tumor segmentation Covers research Issues and the future of deep learning-based brain tumor segmentation
Automatic Brain Tumor Segmentation With Convolutional Neural Network
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Author : Meet Shah
language : en
Publisher:
Release Date : 2020
Automatic Brain Tumor Segmentation With Convolutional Neural Network written by Meet Shah 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.
There are multiple types of Brain Tumors, which can be difficult to evaluate that leads to unpleasant result for the patient. Thus, detection and treatment planning of the brain tumor is the most important factor in the process. Magnetic resonance imaging (MRI) is broadly used technique to evaluate the brain tumors. Manual segmentation of brain tumor from MRI consumes more time and depended on the experience of the machinist. Thus, automated techniques for the segmentation are required to ease the treatment planning. Even in the automated methods for the segmentation is not so easy because of the various types of the brain tumors. Thus, it is necessary to have reliable method for brain tumor segmentation which can measure the tumors efficiently and less time consuming. In this paper, we propose a technique for brain tumor segmentation which is created using U-Net based convolutional neural network. The technique was evaluated on datasets called Multimodal Brain Tumor Image Segmentation (BRATS 2019). This dataset contains more than 76 cases of low-grade tumor and 259 cases of high-grade tumor.
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.
Medical Image Computing And Computer Assisted Intervention Miccai 2020
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Author : Anne L. Martel
language : en
Publisher: Springer Nature
Release Date : 2020-10-02
Medical Image Computing And Computer Assisted Intervention Miccai 2020 written by Anne L. Martel and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-02 with Computers categories.
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
Radiomics And Its Clinical Application
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Author : Jie Tian
language : en
Publisher: Academic Press
Release Date : 2021-06-03
Radiomics And Its Clinical Application written by Jie Tian and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-03 with Computers categories.
The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field. It fully describes three key aspects of radiomic clinical practice: precision diagnosis, the therapeutic effect, and prognostic evaluation, which make radiomics a powerful tool in the clinical setting. This book is a very useful resource for scientists and computer engineers in machine learning and medical image analysis, scientists focusing on antineoplastic drugs, and radiologists, pathologists, oncologists, as well as surgeons wanting to understand radiomics and its potential in clinical practice. An introduction to the concepts of radiomics In-depth presentation of the core technologies and methods Summary of current radiomics research, perspective on the future of radiomics and the challenges ahead An introduction to several platforms that are planned to be built: cooperation, data sharing, software, and application platforms
Machine Learning In Clinical Neuroimaging And Radiogenomics In Neuro Oncology
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Author : Seyed Mostafa Kia
language : en
Publisher: Springer Nature
Release Date : 2020-12-30
Machine Learning In Clinical Neuroimaging And Radiogenomics In Neuro Oncology written by Seyed Mostafa Kia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-30 with Computers categories.
This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.* For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging. For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience. *The workshops were held virtually due to the COVID-19 pandemic.
Machine Intelligence And Signal Analysis
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Author : M. Tanveer
language : en
Publisher: Springer
Release Date : 2018-08-07
Machine Intelligence And Signal Analysis written by M. Tanveer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-07 with Technology & Engineering categories.
The book covers the most recent developments in machine learning, signal analysis, and their applications. It covers the topics of machine intelligence such as: deep learning, soft computing approaches, support vector machines (SVMs), least square SVMs (LSSVMs) and their variants; and covers the topics of signal analysis such as: biomedical signals including electroencephalogram (EEG), magnetoencephalography (MEG), electrocardiogram (ECG) and electromyogram (EMG) as well as other signals such as speech signals, communication signals, vibration signals, image, and video. Further, it analyzes normal and abnormal categories of real-world signals, for example normal and epileptic EEG signals using numerous classification techniques. The book is envisioned for researchers and graduate students in Computer Science and Engineering, Electrical Engineering, Applied Mathematics, and Biomedical Signal Processing.
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.
Computer Aided Detection And Diagnosis In Medical Imaging
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Author : Qiang Li
language : en
Publisher: Taylor & Francis
Release Date : 2015-03-17
Computer Aided Detection And Diagnosis In Medical Imaging written by Qiang Li and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-17 with Medical categories.
Improve the Accurate Detection and Diagnosis of Cancer and Other DiseasesDespite the expansion of the CAD field in recent decades, there is currently no single book dedicated to the development and use of CAD systems. Filling this need, Computer-Aided Detection and Diagnosis in Medical Imaging covers the major technical advances and methodologies s