Graphs In Biomedical Image Analysis Computational Anatomy And Imaging Genetics

Author : M. Jorge Cardoso,Tal Arbel,Enzo Ferrante,Xavier Pennec,Adrian V. Dalca,Sarah Parisot,Sarang Joshi,Nematollah K. Batmanghelich,Aristeidis Sotiras,Mads Nielsen,Mert R. Sabuncu,Tom Fletcher,Li Shen,Stanley Durrleman,Stefan Sommer
Publisher : Springer
Page : 262 pages
File Size : 41,7 Mb
Release : 2017-09-06
Category : Computers
ISBN : 9783319676753

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Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics by M. Jorge Cardoso,Tal Arbel,Enzo Ferrante,Xavier Pennec,Adrian V. Dalca,Sarah Parisot,Sarang Joshi,Nematollah K. Batmanghelich,Aristeidis Sotiras,Mads Nielsen,Mert R. Sabuncu,Tom Fletcher,Li Shen,Stanley Durrleman,Stefan Sommer Pdf

This book constitutes the refereed joint proceedings of the First International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, the 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017, and the Third International Workshop on Imaging Genetics, MICGen 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 7 full papers presented at GRAIL 2017, the 10 full papers presented at MFCA 2017, and the 5 full papers presented at MICGen 2017 were carefully reviewed and selected. The GRAIL papers cover a wide range of graph based medical image analysis methods and applications, including probabilistic graphical models, neuroimaging using graph representations, machine learning for diagnosis prediction, and shape modeling. The MFCA papers deal with theoretical developments in non-linear image and surface registration in the context of computational anatomy. The MICGen papers cover topics in the field of medical genetics, computational biology and medical imaging.

Author : M. Jorge Cardoso,Tal Arbel,Enzo Ferrante,Xavier Pennec,Adrian V. Dalca,Sarah Parisot,Sarang Joshi,Nematollah K. Batmanghelich,Aristeidis Sotiras,Mads Nielsen,Mert R. Sabuncu,Tom Fletcher,Li Shen,Stanley Durrleman,Stefan Sommer
Publisher : Unknown
Page : 250 pages
File Size : 53,7 Mb
Release : 2017
Category : Artificial intelligence
ISBN : 3319676768

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Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics by M. Jorge Cardoso,Tal Arbel,Enzo Ferrante,Xavier Pennec,Adrian V. Dalca,Sarah Parisot,Sarang Joshi,Nematollah K. Batmanghelich,Aristeidis Sotiras,Mads Nielsen,Mert R. Sabuncu,Tom Fletcher,Li Shen,Stanley Durrleman,Stefan Sommer Pdf

Author : Xavier Pennec,Stefan Sommer,Tom Fletcher
Publisher : Academic Press
Page : 636 pages
File Size : 54,7 Mb
Release : 2019-09-02
Category : Computers
ISBN : 9780128147269

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Riemannian Geometric Statistics in Medical Image Analysis by Xavier Pennec,Stefan Sommer,Tom Fletcher Pdf

Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Beyond medical image computing, the methods described in this book may also apply to other domains such as signal processing, computer vision, geometric deep learning, and other domains where statistics on geometric features appear. As such, the presented core methodology takes its place in the field of geometric statistics, the statistical analysis of data being elements of nonlinear geometric spaces. The foundational material and the advanced techniques presented in the later parts of the book can be useful in domains outside medical imaging and present important applications of geometric statistics methodology Content includes: The foundations of Riemannian geometric methods for statistics on manifolds with emphasis on concepts rather than on proofs Applications of statistics on manifolds and shape spaces in medical image computing Diffeomorphic deformations and their applications As the methods described apply to domains such as signal processing (radar signal processing and brain computer interaction), computer vision (object and face recognition), and other domains where statistics of geometric features appear, this book is suitable for researchers and graduate students in medical imaging, engineering and computer science. A complete reference covering both the foundations and state-of-the-art methods Edited and authored by leading researchers in the field Contains theory, examples, applications, and algorithms Gives an overview of current research challenges and future applications

Author : Alejandro Frangi,Jerry Prince,Milan Sonka
Publisher : Academic Press
Page : 700 pages
File Size : 48,9 Mb
Release : 2023-09-20
Category : Technology & Engineering
ISBN : 9780128136584

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Medical Image Analysis by Alejandro Frangi,Jerry Prince,Milan Sonka Pdf

Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. Provides an authoritative description of key concepts and methods Includes tutorial-based sections that clearly explain principles and their application to different medical domains Presents a representative selection of topics to match a modern and relevant approach to medical image computing

Author : Sergey Kushnarev,Anqi Qiu,Laurent Younes
Publisher : World Scientific
Page : 220 pages
File Size : 43,7 Mb
Release : 2019-11-20
Category : Mathematics
ISBN : 9789811200144

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Mathematics Of Shapes And Applications by Sergey Kushnarev,Anqi Qiu,Laurent Younes Pdf

Understanding how a single shape can incur a complex range of transformations, while defining the same perceptually obvious figure, entails a rich and challenging collection of problems, at the interface between applied mathematics, statistics and computer science. The program on Mathematics of Shapes and Applications, was held at the Institute for Mathematical Sciences at the National University of Singapore in 2016. It provided discussions on theoretical developments and numerous applications in computer vision, object recognition and medical imaging.The analysis of shapes is an example of a mathematical problem directly connected with applications while offering deep open challenges to theoretical mathematicians. It has grown, over the past decades, into an interdisciplinary area in which researchers studying infinite-dimensional Riemannian manifolds (global analysis) interact with applied mathematicians, statisticians, computer scientists and biomedical engineers on a variety of problems involving shapes.The volume illustrates this wealth of subjects by providing new contributions on the metric structure of diffeomorphism groups and shape spaces, recent developments on deterministic and stochastic models of shape evolution, new computational methods manipulating shapes, and new statistical tools to analyze shape datasets. In addition to these contributions, applications of shape analysis to medical imaging and computational anatomy are discussed, leading, in particular, to improved understanding of the impact of cognitive diseases on the geometry of the brain.

Author : Danail Stoyanov,Zeike Taylor,Enzo Ferrante,Adrian V. Dalca,Anne Martel,Lena Maier-Hein,Sarah Parisot,Aristeidis Sotiras,Bartlomiej Papiez,Mert R. Sabuncu,Li Shen
Publisher : Springer
Page : 101 pages
File Size : 44,8 Mb
Release : 2018-09-15
Category : Computers
ISBN : 9783030006891

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Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities by Danail Stoyanov,Zeike Taylor,Enzo Ferrante,Adrian V. Dalca,Anne Martel,Lena Maier-Hein,Sarah Parisot,Aristeidis Sotiras,Bartlomiej Papiez,Mert R. Sabuncu,Li Shen Pdf

This book constitutes the refereed joint proceedings of the Second International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and the First International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 6 full papers presented at GRAIL 2018 and the 5 full papers presented at BeYond MIC 2018 were carefully reviewed and selected. The GRAIL papers cover a wide range of develop graph-based models for the analysis of biomedical images and encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts. The Beyond MIC papers cover topics of novel methods with significant imaging and non-imaging components, addressing practical applications and new datasets

Author : Fatos Tunay Yarman Vural,Sharlene D. Newman,Tolga Cukur,Itir Onal Ertugrul
Publisher : Frontiers Media SA
Page : 160 pages
File Size : 40,9 Mb
Release : 2023-03-29
Category : Science
ISBN : 9782832519103

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Machine learning methods for human brain imaging by Fatos Tunay Yarman Vural,Sharlene D. Newman,Tolga Cukur,Itir Onal Ertugrul Pdf

Author : Alexander Oliver Mader
Publisher : BoD – Books on Demand
Page : 252 pages
File Size : 43,8 Mb
Release : 2021-04-15
Category : Medical
ISBN : 9783753480060

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Automatic Localization of Spatially Correlated Key Points in Medical Images by Alexander Oliver Mader Pdf

The task of object localization in medical images is a corner stone of automatic image processing and a prerequisite for other medical imaging tasks. In this thesis, we present a general framework for the automatic detection and localization of spatially correlated key points in medical images based on a conditional random field (CRF). The problem of selecting suitable potential functions (knowledge sources) and defining a reasonable graph topology w.r.t. the dataset is automated by our proposed data-driven CRF optimization. We show how our fairly simple setup can be applied to different medical datasets involving different image dimensionalities (i.e., 2D and 3D), image modalities (i.e., X-ray, CT, MRI) and target objects ranging from 2 to 102 distinct key points by automatically adapting the CRF to the dataset. While the used general "default" configuration represents an easy to transfer setup, it already outperforms other state-of-the-art methods on three out of four datasets. By slightly gearing the proposed approach to the fourth dataset, we further illustrate that the approach is capable of reaching state-of-the-art performance of highly sophisticated and data-specific deep-learning-based approaches. Additionally, we suggest and evaluate solutions for common problems of graph-based approaches such as the reduced search space and thus the potential exclusion of the correct solution, better handling of spatial outliers using latent variables and the incorporation of invariant higher order potential functions. Each extension is evaluated in detail and the whole method is additionally compared to a rivaling convolutional-neural-network-based approach on a hard problem (i.e., the localization of many locally similar repetitive target key points) in terms of exploiting the spatial correlation. Finally, we illustrate how follow-up tasks, segmentation in this case, may benefit from a correct localization by reaching state-of-the-art performance using off-the-shelve methods in combination with our proposed method.

Author : Philipp Grohs,Martin Holler,Andreas Weinmann
Publisher : Springer Nature
Page : 701 pages
File Size : 46,5 Mb
Release : 2020-04-03
Category : Mathematics
ISBN : 9783030313517

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Handbook of Variational Methods for Nonlinear Geometric Data by Philipp Grohs,Martin Holler,Andreas Weinmann Pdf

This book covers different, current research directions in the context of variational methods for non-linear geometric data. Each chapter is authored by leading experts in the respective discipline and provides an introduction, an overview and a description of the current state of the art. Non-linear geometric data arises in various applications in science and engineering. Examples of nonlinear data spaces are diverse and include, for instance, nonlinear spaces of matrices, spaces of curves, shapes as well as manifolds of probability measures. Applications can be found in biology, medicine, product engineering, geography and computer vision for instance. Variational methods on the other hand have evolved to being amongst the most powerful tools for applied mathematics. They involve techniques from various branches of mathematics such as statistics, modeling, optimization, numerical mathematics and analysis. The vast majority of research on variational methods, however, is focused on data in linear spaces. Variational methods for non-linear data is currently an emerging research topic. As a result, and since such methods involve various branches of mathematics, there is a plethora of different, recent approaches dealing with different aspects of variational methods for nonlinear geometric data. Research results are rather scattered and appear in journals of different mathematical communities. The main purpose of the book is to account for that by providing, for the first time, a comprehensive collection of different research directions and existing approaches in this context. It is organized in a way that leading researchers from the different fields provide an introductory overview of recent research directions in their respective discipline. As such, the book is a unique reference work for both newcomers in the field of variational methods for non-linear geometric data, as well as for established experts that aim at to exploit new research directions or collaborations. Chapter 9 of this book is available open access under a CC BY 4.0 license at link.springer.com.

Author : Nils A. Baas,Gunnar E. Carlsson,Gereon Quick,Markus Szymik,Marius Thaule
Publisher : Springer Nature
Page : 522 pages
File Size : 50,7 Mb
Release : 2020-06-25
Category : Mathematics
ISBN : 9783030434083

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Topological Data Analysis by Nils A. Baas,Gunnar E. Carlsson,Gereon Quick,Markus Szymik,Marius Thaule Pdf

This book gathers the proceedings of the 2018 Abel Symposium, which was held in Geiranger, Norway, on June 4-8, 2018. The symposium offered an overview of the emerging field of "Topological Data Analysis". This volume presents papers on various research directions, notably including applications in neuroscience, materials science, cancer biology, and immune response. Providing an essential snapshot of the status quo, it represents a valuable asset for practitioners and those considering entering the field.

Author : Ilya Bychkov,Valery A. Kalyagin,Panos M. Pardalos,Oleg Prokopyev
Publisher : Springer Nature
Page : 251 pages
File Size : 48,9 Mb
Release : 2020-02-22
Category : Mathematics
ISBN : 9783030371579

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Network Algorithms, Data Mining, and Applications by Ilya Bychkov,Valery A. Kalyagin,Panos M. Pardalos,Oleg Prokopyev Pdf

This proceedings presents the result of the 8th International Conference in Network Analysis, held at the Higher School of Economics, Moscow, in May 2018. The conference brought together scientists, engineers, and researchers from academia, industry, and government. Contributions in this book focus on the development of network algorithms for data mining and its applications. Researchers and students in mathematics, economics, statistics, computer science, and engineering find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks. Machine learning techniques in network settings including community detection, clustering, and biclustering algorithms are presented with applications to social network analysis.

Author : Frank Nielsen,Frédéric Barbaresco
Publisher : Springer Nature
Page : 929 pages
File Size : 43,9 Mb
Release : 2021-07-14
Category : Computers
ISBN : 9783030802097

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Geometric Science of Information by Frank Nielsen,Frédéric Barbaresco Pdf

This book constitutes the proceedings of the 5th International Conference on Geometric Science of Information, GSI 2021, held in Paris, France, in July 2021. The 98 papers presented in this volume were carefully reviewed and selected from 125 submissions. They cover all the main topics and highlights in the domain of geometric science of information, including information geometry manifolds of structured data/information and their advanced applications. The papers are organized in the following topics: Probability and statistics on Riemannian Manifolds; sub-Riemannian geometry and neuromathematics; shapes spaces; geometry of quantum states; geometric and structure preserving discretizations; information geometry in physics; Lie group machine learning; geometric and symplectic methods for hydrodynamical models; harmonic analysis on Lie groups; statistical manifold and Hessian information geometry; geometric mechanics; deformed entropy, cross-entropy, and relative entropy; transformation information geometry; statistics, information and topology; geometric deep learning; topological and geometrical structures in neurosciences; computational information geometry; manifold and optimization; divergence statistics; optimal transport and learning; and geometric structures in thermodynamics and statistical physics.

Author : Kaijian Xia,Yizhang Jiang,Yu-Dong Zhang,Mohammad Khosravi,Yuanpeng Zhang
Publisher : Frontiers Media SA
Page : 754 pages
File Size : 54,7 Mb
Release : 2022-11-09
Category : Science
ISBN : 9782832504628

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Advanced Computational Intelligence Methods for Processing Brain Imaging Data by Kaijian Xia,Yizhang Jiang,Yu-Dong Zhang,Mohammad Khosravi,Yuanpeng Zhang Pdf

Author : Adrian Dalca,Kayhan N. Batmanghelich,Mert Sabuncu,Li Shen
Publisher : Academic Press
Page : 182 pages
File Size : 52,7 Mb
Release : 2017-09-22
Category : Technology & Engineering
ISBN : 9780128139691

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Imaging Genetics by Adrian Dalca,Kayhan N. Batmanghelich,Mert Sabuncu,Li Shen Pdf

Imaging Genetics presents the latest research in imaging genetics methodology for discovering new associations between imaging and genetic variables, providing an overview of the state-of the-art in the field. Edited and written by leading researchers, this book is a beneficial reference for students and researchers, both new and experienced, in this growing area. The field of imaging genetics studies the relationships between DNA variation and measurements derived from anatomical or functional imaging data, often in the context of a disorder. While traditional genetic analyses rely on classical phenotypes like clinical symptoms, imaging genetics can offer richer insights into underlying, complex biological mechanisms. Contains an introduction describing how the field has evolved to the present, together with perspectives on its future direction and challenges Describes novel application domains and analytic methods that represent the state-of-the-art in the burgeoning field of imaging genetics Introduces a novel, large-scale analytic framework that involves multi-site, image-wide, genome-wide associations

Author : Sushruta Mishra,Hrudaya Kumar Tripathy,Pradeep Mallick,Khaled Shaalan
Publisher : Springer Nature
Page : 503 pages
File Size : 43,6 Mb
Release : 2022-04-19
Category : Technology & Engineering
ISBN : 9789811910760

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Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis by Sushruta Mishra,Hrudaya Kumar Tripathy,Pradeep Mallick,Khaled Shaalan Pdf

The book discusses how augmented intelligence can increase the efficiency and speed of diagnosis in healthcare organizations. The concept of augmented intelligence can reflect the enhanced capabilities of human decision-making in clinical settings when augmented with computation systems and methods. It includes real-life case studies highlighting impact of augmented intelligence in health care. The book offers a guided tour of computational intelligence algorithms, architecture design, and applications of learning in healthcare challenges. It presents a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It also presents specific applications of augmented intelligence in health care, and architectural models and frameworks-based augmented solutions.