Multimodal Brain Tumor Segmentation And Beyond
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Multimodal Brain Tumor Segmentation And Beyond
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Author : Bjoern Menze
language : en
Publisher: Frontiers Media SA
Release Date : 2021-08-10
Multimodal Brain Tumor Segmentation And Beyond written by Bjoern Menze 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 2021-08-10 with Science categories.
Multimodal Brain Image Fusion Methods Evaluations And Applications
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Author : Yu Liu
language : en
Publisher: Frontiers Media SA
Release Date : 2023-02-06
Multimodal Brain Image Fusion Methods Evaluations And Applications written by Yu Liu 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 2023-02-06 with Science categories.
Multi Modal Multi Temporal Brain Tumor Segmentation Growth Analysis And Texture Based Classification
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Author : Esther J. Alberts
language : en
Publisher:
Release Date : 2019
Multi Modal Multi Temporal Brain Tumor Segmentation Growth Analysis And Texture Based Classification written by Esther J. Alberts 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.
Multi Modal Multi Temporal Brain Tumor Segmentation Growth Analysis And Texture Based Classification
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Author : Esther Alberts
language : en
Publisher:
Release Date : 2019
Multi Modal Multi Temporal Brain Tumor Segmentation Growth Analysis And Texture Based Classification written by Esther Alberts 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.
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.
Improving The Generalizability Of Convolutional Neural Networks For Brain Tumor Segmentation In The Post Treatment Setting
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Author : Jacob Ellison
language : en
Publisher:
Release Date : 2020
Improving The Generalizability Of Convolutional Neural Networks For Brain Tumor Segmentation In The Post Treatment Setting written by Jacob Ellison 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.
Current encoder-decoder convolutional neural networks (CNN) used in automated glioma lesion segmentation and volumetric measurements perform well on newly diagnosed lesions that have not received any treatment. However, there are challenges in generalizability for patients after treatment, including at the time of suspected recurrence. This results in decreased translation to clinical use in the post-treatment setting where it is needed the most. A potential reason is that these deep learning models are primarily trained on a singular curated dataset and demonstrate decreased performance when they are tested in situations with unseen variations to disease states, scanning protocols or equipment, and operators. While using a highly curated dataset does have the benefit of standardizing comparison of models, it comes with some significant drawbacks to generalizability. The primary source of images used to train current models for glioma segmentation is the BraTS (Multimodal Brain Tumor Image Segmentation Benchmark) dataset. The image domain of the BraTS dataset is large, including high- and low-grade tumors, varying acquisition resolution, and scans from multi-center studies. Despite this, it may still lack sufficient feature representation in the target clinical imaging domain. Here we address generalizability to the disease state of post-treatment glioma. The current BraTS dataset consists entirely of images obtained from newly diagnosed patients who have not undergone surgical resection, received adjuvant treatment, or shown significant disease progression, all of which can greatly alter the characteristics of these lesions. To improve the clinical utility of deep learning models for glioma segmentation, they must accommodate variations in signal intensity that may arise as a result of resection, tissue damage (treatment induced or otherwise), or progression. We compared models trained on either BraTS data, UCSF acquired post-treatment glioma data, UCSF acquired newly diagnosed glioma data, and various combinations of these data, to determine the effect of including images with features unique to treated gliomas into training the networks on segmentation performance in the post-treatment domain. Although an absolute threshold training inclusion value for generalization of segmentation networks to post-treatment glioma patients has not been established, we found that with 200 total training volumes, models trained with greater than or equal to 30% of the training images from patients with prior treatment received the greatest performance gains when testing in this domain. Additionally, we found that after this threshold is met, additional images from newly diagnosed patients did not negatively impact segmentation performance on patients with treated gliomas. We also developed a pre-processing pipeline and implemented a loss penalty term that incorporates cavity distance relationships to the tumor into weighting a cross entropy loss term. The aim of this was to bias the network weights to morphological features of the image relevant to pathologies that are prevalent post-treatment. This may either be used as an initialization for training with an available larger dataset such as BraTS or used to finetune a transferred network that has not seen sufficient post-treatment glioma images during training in order to allow domain adaptation with fewer training data from this disease state. Preliminary results show qualitatively more desirable segmentations of tumor lesions with respect to cavities and small disconnected components in selected examples that are worthy of further analysis with alternate training configurations, more focused performance assessments, and larger cohorts. Here, we will evaluate these techniques as potential solutions to improve the generalizability of CNN tumor segmentation to post- treatment glioma, as well as provide a framework for further data augmentation based on augmenting the boundary of these lesions.
Brainlesion Glioma Multiple Sclerosis Stroke And Traumatic Brain Injuries
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Author : Alessandro Crimi
language : en
Publisher: Springer
Release Date : 2018-02-16
Brainlesion Glioma Multiple Sclerosis Stroke And Traumatic Brain Injuries written by Alessandro Crimi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-16 with Computers categories.
This book constitutes revised selected papers from the Third International MICCAI Brainlesion Workshop, BrainLes 2017, as well as the International Multimodal Brain Tumor Segmentation, BraTS, and White Matter Hyperintensities, WMH, segmentation challenges, which were held jointly at the Medical Image computing for Computer Assisted Intervention Conference, MICCAI, in Quebec City, Canada, in September 2017. The 40 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; and ischemic stroke lesion image segmentation.
Brainlesion Glioma Multiple Sclerosis Stroke And Traumatic Brain Injuries
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Author : Alessandro Crimi
language : en
Publisher: Springer
Release Date : 2016-03-19
Brainlesion Glioma Multiple Sclerosis Stroke And Traumatic Brain Injuries written by Alessandro Crimi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-19 with Computers categories.
This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Brain Lesion (BrainLes), Brain Tumor Segmentation (BRATS) and Ischemic Stroke Lesion Segmentation (ISLES), held in Munich, Germany, on October 5, 2015, in conjunction with the International Conference on Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015. The 25 papers presented in this volume were carefully reviewed and selected from 28 submissions. They are grouped around the following topics: brain lesion image analysis; brain tumor image segmentation; ischemic stroke lesion image segmentation.
Intelligence In Big Data Technologies Beyond The Hype
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Author : J. Dinesh Peter
language : en
Publisher: Springer Nature
Release Date : 2020-07-25
Intelligence In Big Data Technologies Beyond The Hype written by J. Dinesh Peter and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-25 with Technology & Engineering categories.
This book is a compendium of the proceedings of the International Conference on Big-Data and Cloud Computing. The papers discuss the recent advances in the areas of big data analytics, data analytics in cloud, smart cities and grid, etc. This volume primarily focuses on the application of knowledge which promotes ideas for solving problems of the society through cutting-edge big-data technologies. The essays featured in this proceeding provide novel ideas that contribute for the growth of world class research and development. It will be useful to researchers in the area of advanced engineering sciences.
Computational Intelligence In Pattern Recognition
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Author : Asit Kumar Das
language : en
Publisher: Springer
Release Date : 2019-08-17
Computational Intelligence In Pattern Recognition written by Asit Kumar Das and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-17 with Technology & Engineering categories.
This book presents practical development experiences in different areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.