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Efficient Magnetic Resonance Brain Image Registration And High Performance Registration Based Brain Image Segmentation
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Book Synopsis Efficient Magnetic Resonance Brain Image Registration and High Performance Registration-based Brain Image Segmentation by : Yishan Luo
Download or read book Efficient Magnetic Resonance Brain Image Registration and High Performance Registration-based Brain Image Segmentation written by Yishan Luo and published by . This book was released on 2012 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Brain Tumor MRI Image Segmentation Using Deep Learning Techniques by : Jyotismita Chaki
Download or read book Brain Tumor MRI Image Segmentation Using Deep Learning Techniques written by Jyotismita Chaki and published by Academic Press. This book was released on 2021-11-27 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis High Performance Algorithms for Medical Image Registration with Applications in Neuroradiology by : Naveen Himthani
Download or read book High Performance Algorithms for Medical Image Registration with Applications in Neuroradiology written by Naveen Himthani and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation concerns the design, analysis and High-Performance Computing (HPC) implementation of fast algorithms for large deformation diffeomorphic registration and its application in quantifying abnormal anatomical deformations in Magnetic Resonance Image (MRI) scans of brain tumor patients. Image registration finds point correspondences between two images by solving an optimization problem. It is a fundamental and computationally expensive operation that finds applications in computer vision and medical image analysis. Diffeomorphic registration is a non-convex and nonlinear inverse problem and, as a result, presents significant numerical and computational challenges. Designing and implementing efficient and accurate numerical schemes on modern computer architectures is the key to accelerating and sometimes even enabling the development of image analysis workflows. In this dissertation, we contribute to several aspects of diffeomorphic registration: (i) a novel preconditioner that improves performance and scalability, (ii) algorithms and their scalable implementation on heterogeneous compute architectures, and (ii) applications in neuroradiology. Our work on diffeomorphic image registration is based on CLAIRE – a formulation, algorithmic framework, and software developed at the University of Texas at Austin. As the first highlight of our contributions, we introduced a novel two-level Hessian preconditioner that results in an improvement of 2.5× in CLAIRE’s performance. As a second highlight, our optimized HPC implementation yields orders of magnitude speedup as CLAIRE now supports GPU architectures and distributed memory parallelism via GPU-aware message passing interface (MPI). CLAIRE can register clinical-grade brain MRI scans of size 2563 in under 5 seconds on a single NVIDIA V100 GPU. For research-grade high-resolution volumetric images, e.g., mouse brain CLARITY images of size 2816 × 3016 × 1162, CLAIRE takes under 30 minutes using 256 NVIDIA V100 GPUs on the Texas Advanced Computing Center’s (TACC) Longhorn supercomputer. To the best of our knowledge, CLAIRE is the most scalable image registration algorithm and software. CLAIRE has been open-sourced under the GNU v3 license and is available on Github at https://github.com/andreasmang/claire. Our target clinical application concerns the utilization of image registration to characterize the mass effect in MRI scans of patients with glioblastoma, a fatal brain cancer. Mass effect is the mechanical deformation in surrounding healthy tissue caused by the growing tumor. The location and degree of mass effect could aid in differential diagnosis and treatment planning. Towards this end, we introduce an algorithm that integrates CLAIRE, statistical analysis for abnormality detection, and machine learning to quantify and localize mass effect. Given a patient’s brain tumor scan, we generate a clinical summary with (i) an estimate of the degree of mass effect along with a severity label – mild, moderate, or severe with up to 62% accuracy, (ii) a heatmap of mass effect for the brain scan and, (iii) a list of specific anatomical regions, e.g. frontal lobe, which is statistically likely to possess significant mass effect
Book Synopsis Brain Magnetic Resonance Image Analysis by : Liping Wang
Download or read book Brain Magnetic Resonance Image Analysis written by Liping Wang and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Brain Magnetic Resonance Image Analysis by :
Download or read book Brain Magnetic Resonance Image Analysis written by and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Medical Image Registration by : Joseph V. Hajnal
Download or read book Medical Image Registration written by Joseph V. Hajnal and published by CRC Press. This book was released on 2001-06-27 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image registration is the process of systematically placing separate images in a common frame of reference so that the information they contain can be optimally integrated or compared. This is becoming the central tool for image analysis, understanding, and visualization in both medical and scientific applications. Medical Image Registration provid
Book Synopsis Computer Recognition Systems by : Marek Kurzynski
Download or read book Computer Recognition Systems written by Marek Kurzynski and published by Springer. This book was released on 2005-05-04 with total page 903 pages. Available in PDF, EPUB and Kindle. Book excerpt: th This book contains papers accepted for presentation at the 4 International Conference on Computer Recognition Systems CORES'05, May 22-25, 2005, Rydzyna Castle (Poland), This conference is a continuation of a series of con ferences on similar topics (KOSYR) organized each second year, since 1999, by the Chair of Systems and Computer Networks, Wroclaw University of Tech nology. An increasing interest to those conferences paid not only by home but also by foreign participants inspired the organizers to transform them into conferences of international range. Our expectations that the community of specialists in computer recognizing systems will find CORES'05 a proper form of maintaining the tradition of the former conferences have been confirmed by a large number of submitted papers. Alas, organizational constraints caused a necessity to narrow the acceptance criteria so that only 100 papers have been finally included into the conference program. The area covered by accepted papers is still very large and it shows how vivacious is scientific activity in the domain of computer recognition methods and systems. It contains vari ous theoretical approaches to the recognition problem based on mathematical statistics, fuzzy sets, morphological methods, wavelets, syntactic methods, genetic algorithms, artificial neural networks, ontological models, etc. Most attention is still paid to visual objects recognition; however, acoustic, tex tual and other objects are also considered. Among application areas medical problems are in majority; recognition of faces, speech signals and textual in formation processing methods being also investigated.
Book Synopsis Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies by : Ayman S. El-Baz
Download or read book Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies written by Ayman S. El-Baz and published by Springer Science & Business Media. This book was released on 2011-05-04 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advances in image guided surgery for cancer treatment, the role of image segmentation and registration has become very critical. The central engine of any image guided surgery product is its ability to quantify the organ or segment the organ whether it is a magnetic resonance imaging (MRI) and computed tomography (CT), X-ray, PET, SPECT, Ultrasound, and Molecular imaging modality. Sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures present in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning system designs. The focus of this book in towards the state of the art techniques in the area of image segmentation and registration.
Book Synopsis Efficient Multi-modal Image Registration Based on Gradient Orientations of Minimal Uncertainty by : Dante De Nigris Moreno
Download or read book Efficient Multi-modal Image Registration Based on Gradient Orientations of Minimal Uncertainty written by Dante De Nigris Moreno and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis presents a general framework for the registration of medicalimages across multiple clinical contexts involving rigid and non-rigidapplications. The proposed framework relies on gradient orientations asprimitive geometric descriptors so as to locally assess image similarity basedon orientation alignment and evaluates the metric on sparse locationscorresponding to anatomical boundaries of interest. The two main advantagesbrought forward by the proposed approach are: (1) a substantial reduction incomputational complexity and processing time and (2) a significant improvementin robustness against multi-modal contexts with widely different imageformation models and significant non-homogeneities.The proposed approach is evaluated in multiple clinical contexts and comparedagainst state-of-the-art techniques. In the context of neurosurgery, imageregistration can be employed so as to update a pre-operative magneticresonance image (MRI) based on an intra-operative ultrasound volume. Theproposed approach is evaluated in this challenging time-sensitive scenario andis shown to provide robust performance with sub-second processing times. Inthe context of the rigid registration of computational tomography (CT) and MRIbrain volumes, the proposed approach is evaluated with a publicly availabledataset and compared against previously proposed techniques. The quantitativeresults demonstrate that the proposed approach can employ highly reducedsampling rates (e.g. only 0.05% of the voxels in the image) while stillyielding a median registration error inferior to 1mm. In the context of thenon-rigid registration of inter-patient MRI brain volumes, the proposedapproach is evaluated with a publicly available dataset which measuresregistration accuracy in terms of the agreement of spatially mapped labelswith expert annotated labels. The use of such dataset allows for a fair andunbiased comparison with over fourteen competing techniques. The quantitativeresults show that the proposed approach achieves slightly inferior accuracythan the top performing method but with only one sixth of the processing timerequired by the alternative technique. Finally, the proposed approach isevaluated in the context of automatic brain lesion detection which relies onhealthy tissue probability maps obtained via registration to a brain atlas.The quantitative comparison against two leading image registration techniquesshows that the proposed approach can lead to a slightly improved performanceof brain lesion detection algorithms while requiring only one sixth of theprocessing time used by competing registration approaches." --
Book Synopsis Machine Learning and Medical Imaging by : Guorong Wu
Download or read book Machine Learning and Medical Imaging written by Guorong Wu and published by Academic Press. This book was released on 2016-08-11 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques
Book Synopsis Magnetic Resonance Brain Imaging by : Jörg Polzehl
Download or read book Magnetic Resonance Brain Imaging written by Jörg Polzehl and published by Springer Nature. This book was released on 2019-09-25 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the modeling and analysis of magnetic resonance imaging (MRI) data acquired from the human brain. The data processing pipelines described rely on R. The book is intended for readers from two communities: Statisticians who are interested in neuroimaging and looking for an introduction to the acquired data and typical scientific problems in the field; and neuroimaging students wanting to learn about the statistical modeling and analysis of MRI data. Offering a practical introduction to the field, the book focuses on those problems in data analysis for which implementations within R are available. It also includes fully worked examples and as such serves as a tutorial on MRI analysis with R, from which the readers can derive their own data processing scripts. The book starts with a short introduction to MRI and then examines the process of reading and writing common neuroimaging data formats to and from the R session. The main chapters cover three common MR imaging modalities and their data modeling and analysis problems: functional MRI, diffusion MRI, and Multi-Parameter Mapping. The book concludes with extended appendices providing details of the non-parametric statistics used and the resources for R and MRI data.The book also addresses the issues of reproducibility and topics like data organization and description, as well as open data and open science. It relies solely on a dynamic report generation with knitr and uses neuroimaging data publicly available in data repositories. The PDF was created executing the R code in the chunks and then running LaTeX, which means that almost all figures, numbers, and results were generated while producing the PDF from the sources.
Book Synopsis A Parameter-Efficient Deep Dense Residual Convolutional Neural Network for Volumetric Brain Tissue Segmentation from Magnetic Resonance Images by : Ramesh Basnet
Download or read book A Parameter-Efficient Deep Dense Residual Convolutional Neural Network for Volumetric Brain Tissue Segmentation from Magnetic Resonance Images written by Ramesh Basnet and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain tissue segmentation is a common medical image processing problem that deals with identifying a region of interest in the human brain from medical scans. It is a fundamental step towards neuroscience research and clinical diagnosis. Magnetic resonance (MR) images are widely used for segmentation in view of their non-invasive acquisition, and high spatial resolution and various contrast information. Accurate segmentation of brain tissues from MR images is very challenging due to the presence of motion artifacts, low signal-to-noise ratio, intensity overlaps, and intra- and inter-subject variability. Convolutional neural networks (CNNs) recently employed for segmentation provide remarkable advantages over the traditional and manual segmentation methods, however, their complex architectures and the large number of parameters make them computationally expensive and difficult to optimize. In this thesis, a novel learning-based algorithm using a three-dimensional deep convolutional neural network is proposed for efficient parameter reduction and compact feature representation to learn end-to-end mapping of T1-weighted (T1w) and/or T2-weighted (T2w) brain MR images to the probability scores of each voxel belonging to the different labels of brain tissues, namely, white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) for segmentation. The basic idea in the proposed method is to use densely connected convolutional layers and residual skip-connections to increase representation capacity, facilitate better gradient flow, improve learning, and significantly reduce the number of parameters in the network. The network is independently trained on three different loss functions, cross-entropy, dice similarity, and a combination of the two and the results are compared with each other to investigate better loss function for the training. The model has the number of network parameters reduced by a significant amount compared to that of the state-of-the-art methods in brain tissue segmentation. Experiments are performed using the single-modality IBSR18 dataset containing high-resolution T1-weighted MR scans of diverse age groups, and the multi-modality iSeg-2017 dataset containing T1w and T2w MR scans of infants. It is shown that the proposed method provides the best performance on the test sets of both datasets amongst all the existing deep-learning based methods for brain tissue segmentation using the MR images and achieves competitive performance in the iSeg-2017 challenge with the number of parameters that is 47% to 98% lower than that of the other deep-learning based architectures.
Book Synopsis Contour-based Brain Image Registration for Perfusion-diffusion Magnetic Resonance Imaging Studies by : Yanhong Chu
Download or read book Contour-based Brain Image Registration for Perfusion-diffusion Magnetic Resonance Imaging Studies written by Yanhong Chu and published by . This book was released on 1992 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis MAPPING: MAnagement and Processing of Images for Population ImagiNG by : Michel Dojat
Download or read book MAPPING: MAnagement and Processing of Images for Population ImagiNG written by Michel Dojat and published by Frontiers Media SA. This book was released on 2017-09-04 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Several recent papers underline methodological points that limit the validity of published results in imaging studies in the life sciences and especially the neurosciences (Carp, 2012; Ingre, 2012; Button et al., 2013; Ioannidis, 2014). At least three main points are identified that lead to biased conclusions in research findings: endemic low statistical power and, selective outcome and selective analysis reporting. Because of this, and in view of the lack of replication studies, false discoveries or solutions persist. To overcome the poor reliability of research findings, several actions should be promoted including conducting large cohort studies, data sharing and data reanalysis. The construction of large-scale online databases should be facilitated, as they may contribute to the definition of a “collective mind” (Fox et al., 2014) facilitating open collaborative work or “crowd science” (Franzoni and Sauermann, 2014). Although technology alone cannot change scientists’ practices (Wicherts et al., 2011; Wallis et al., 2013, Poldrack and Gorgolewski 2014; Roche et al. 2014), technical solutions should be identified which support a more “open science” approach. Also, the analysis of the data plays an important role. For the analysis of large datasets, image processing pipelines should be constructed based on the best algorithms available and their performance should be objectively compared to diffuse the more relevant solutions. Also, provenance of processed data should be ensured (MacKenzie-Graham et al., 2008). In population imaging this would mean providing effective tools for data sharing and analysis without increasing the burden on researchers. This subject is the main objective of this research topic (RT), cross-listed between the specialty section “Computer Image Analysis” of Frontiers in ICT and Frontiers in Neuroinformatics. Firstly, it gathers works on innovative solutions for the management of large imaging datasets possibly distributed in various centers. The paper of Danso et al. describes their experience with the integration of neuroimaging data coming from several stroke imaging research projects. They detail how the initial NeuroGrid core metadata schema was gradually extended for capturing all information required for future metaanalysis while ensuring semantic interoperability for future integration with other biomedical ontologies. With a similar preoccupation of interoperability, Shanoir relies on the OntoNeuroLog ontology (Temal et al., 2008; Gibaud et al., 2011; Batrancourt et al., 2015), a semantic model that formally described entities and relations in medical imaging, neuropsychological and behavioral assessment domains. The mechanism of “Study Card” allows to seamlessly populate metadata aligned with the ontology, avoiding fastidious manual entrance and the automatic control of the conformity of imported data with a predefined study protocol. The ambitious objective with the BIOMIST platform is to provide an environment managing the entire cycle of neuroimaging data from acquisition to analysis ensuring full provenance information of any derived data. Interestingly, it is conceived based on the product lifecycle management approach used in industry for managing products (here neuroimaging data) from inception to manufacturing. Shanoir and BIOMIST share in part the same OntoNeuroLog ontology facilitating their interoperability. ArchiMed is a data management system locally integrated for 5 years in a clinical environment. Not restricted to Neuroimaging, ArchiMed deals with multi-modal and multi-organs imaging data with specific considerations for data long-term conservation and confidentiality in accordance with the French legislation. Shanoir and ArchiMed are integrated into FLI-IAM1, the national French IT infrastructure for in vivo imaging.
Book Synopsis Functional Magnetic Resonance Imaging Processing by : Xingfeng Li
Download or read book Functional Magnetic Resonance Imaging Processing written by Xingfeng Li and published by Springer Science & Business Media. This book was released on 2013-09-14 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: With strong numerical and computational focus, this book serves as an essential resource on the methods for functional neuroimaging analysis, diffusion weighted image analysis, and longitudinal VBM analysis. It includes four MRI image modalities analysis methods. The first covers the PWI methods, which is the basis for understanding cerebral flow in human brain. The second part, the book’s core, covers fMRI methods in three specific domains: first level analysis, second level analysis, and effective connectivity study. The third part covers the analysis of Diffusion weighted image, i.e. DTI, QBI and DSI image analysis. Finally, the book covers (longitudinal) VBM methods and its application to Alzheimer’s disease study.
Book Synopsis Probabilistic Modeling for Segmentation in Magnetic Resonance Images of the Human Brain by : Michael Wels
Download or read book Probabilistic Modeling for Segmentation in Magnetic Resonance Images of the Human Brain written by Michael Wels and published by Logos Verlag Berlin GmbH. This book was released on 2010 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book the fully automatic generation of semantic annotations for medical imaging data by means of medical image segmentation and labeling is addressed. In particular, the focus is on the segmentation of the human brain and related structures from magnetic resonance imaging (MRI) data. Three novel probabilistic methods from the field of database-guided knowledge-based medical image segmentation are presented. Each of the methods is applied to one of three MRI segmentation scenarios: 1) 3-D MRI brain tissue classification and intensity non-uniformity correction, 2) pediatric brain cancer segmentation in multi-spectral 3-D MRI, and 3) 3-D MRI anatomical brain structure segmentation. All the newly developed methods make use of domain knowledge encoded by probabilistic boosting-trees (PBT), which is a recent machine learning technique. For all the methods uniform probabilistic formalisms are presented that group the methods into the broader context of probabilistic modeling for the purpose of image segmentation. It is shown by comparison with other methods from the literature that in all the scenarios the newly developed algorithms in most cases give more accurate results and have a lower computational cost. Evaluation on publicly available benchmarking data sets ensures reliable comparability of the results to those of other current and future methods. One of the methods successfully participated in the ongoing online caudate segmentation challenge (www.cause07.org), where it ranks among the top five methods for this particular segmentation scenario.
Book Synopsis Knowledge-based image segmentation using deformable registration: application to brain MRI images by : Xiangbo Lin
Download or read book Knowledge-based image segmentation using deformable registration: application to brain MRI images written by Xiangbo Lin and published by . This book was released on 2009 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research goal of this thesis is a contribution to the intra-modality inter-subject non-rigid medical image registration and the segmentation of 3D brain MRI images in normal case. The well-known Demons non-rigid algorithm is studied, where the image intensities are used as matching features. A new force computation equation is proposed to solve the mismatch problem in some regions. The efficiency is shown through numerous evaluations on simulated and real data. For intensity based inter-subject registration, normalizing the image intensities is important for satisfying the intensity correspondence requirements. A non-rigid registration method combining both intensity and spatial normalizations is proposed. Topology constraints are introduced in the deformable model to preserve an expected property in homeomorphic targets registration. The solution comes from the correction of displacement points with negative Jacobian determinants. Based on the registration, a segmentation method of the internal brain structures is studied. The basic principle is represented by ontology of prior shape knowledge of target internal structure. The shapes are represented by a unified distance map computed from the atlas and the deformed atlas, and then integrated into the similarity metric of the cost function. A balance parameter is used to adjust the contributions of the intensity and shape measures. The influence of different parameters of the method and comparisons with other registration methods were performed. Very good results are obtained on the segmentation of different internal structures of the brain such as central nuclei and hippocampus.
