Автор: Suganya, R. Название: Big data in medical image processing ISBN: 1138557242 ISBN-13(EAN): 9781138557246 Издательство: Taylor&Francis Рейтинг: Цена: 31390.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book provides an automated system that could retrieve images based on user`s interest to a point of providing decision support. It will help medical analysts to take informed decisions before planning treatment and surgery. It will also be useful to researchers who are working in problems involved in medical imaging.
Автор: Zheng Название: Marginal Space Learning for Medical Image Analysis ISBN: 1493905996 ISBN-13(EAN): 9781493905997 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Automatic detection and segmentation of anatomical structures in medical images are prerequisites to subsequent image measurements and disease quantification, and therefore have multiple clinical applications.
Описание: Multi-atlas segmentation of the aorta from 4D flow MRI: comparison of several fusion strategie.- Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data.- Coronary Artery Centerline Refinement using GCN Trained with Synthetic Data.- Novel imaging biomarkers to evaluate heart dysfunction post-chemotherapy: a preclinical MRI feasibility study.- A bi-atrial statistical shape model as a basis to classify left atrial enlargement from simulated and clinical 12-lead ECGs.- Vessel Extraction and Analysis of Aortic Dissection.- The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images.- Characterizing myocardial ischemia and reperfusion patterns with hierarchical manifold learning.- Generating Subpopulation-Specific Biventricular Anatomy Models Using Conditional Point Cloud Variational Autoencoders.- Improved AI-based Segmentation of Apical and Basal Slices from Clinical Cine CMR.- Mesh Convolutional Neural Networks for Wall Shear Stress Estimation in 3D Artery Models.- Hierarchical multi-modality prediction model to assess obesity-related remodelling.- Neural Angular Plaque Characterization: Automated Quantification of Polar Distributionfor Plaque Composition.- Simultaneous Segmentation and Motion Estimation of Left Ventricular Myocardium in 3D Echocardiography using Multi-task Learning.- Statistical shape analysis of the tricuspid valve in hypoplastic left heart syndrome.- An Unsupervised 3D Recurrent Neural Networkfor Slice Misalignment Correction in CardiacMR Imaging.- Unsupervised Multi-Modality RegistrationNetwork based on Spatially Encoded Gradient Information.- In-silico analysis of device-related thrombosis for different left atrial appendage occluder settings.- Valve flattening with functional biomarkers for the assessment of mitral valve repair.- Multi-modality cardiac segmentation via mixing domains for unsupervised adaptation.- Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction.- Cross-domain Artefact Correction of Cardiac MRI.- Detection and Classification of Coronary Artery Plaques in Coronary Computed Tomography Angiography Using 3D CNN.- Predicting 3D Cardiac Deformations With Point Cloud Autoencoders.- Influence of morphometric and mechanical factors in thoracic aorta finite element modeling.- Right Ventricle Segmentation via Registration and Multi-input Modalities in Cardiac Magnetic Resonance Imaging from Multi-Disease, Multi-View and Multi-Center.- Using MRI-specific Data Augmentation to Enhance the Segmentation of Right Ventricle in Multi-disease, Multi-center and Multi-view Cardiac MRI.- Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition.- Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation.- Multi-view SA-LA Net: A framework for simultaneous segmentation of RV on multi-view cardiac MR Images.- Right ventricular segmentation in multi-view cardiac MRI using a unified U-net model.- Deformable Bayesian Convolutional Networks for Disease-Robust Cardiac MRI Segmentation.- Consistency based Co-Segmentation for Multi-View Cardiac MRI using Vision Transformer.- Refined Deep Layer Aggregation for Multi-Disease, Multi-View & Multi-Center Cardiac MR Segmentation.- A Multi-View Cross-Over Attention U-Net Cascade With Fourier Domain Adaptation For Multi-Domain Cardiac MRI Segmentation.- Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI using Efficient Late-Ensemble Deep Learning Approach.- Automated Segmentation of the Right Ventricle from Magnetic Resonance Imaging Using Deep Convolutional Neural Networks.- 3D right ventricle reconstruction from 2D U-Net segmentation of sparse short-axis and 4-chamber cardiac cine MRI views.- Late Fusion U-Net with GAN-based Augmentation for Generalizable Cardiac MRI Segmentation.- Using Out-of-Distribution Detection for Model Refinement in Cardiac Im
Автор: Zhou,S. Kevin Название: Medical Image Recognition, Segmentation And Parsing ISBN: 0128025816 ISBN-13(EAN): 9780128025819 Издательство: Elsevier Science Рейтинг: Цена: 18022.00 р. Наличие на складе: Поставка под заказ.
Автор: Yoo, Terry S. Название: Insight into Images ISBN: 1568812175 ISBN-13(EAN): 9781568812175 Издательство: Taylor&Francis Рейтинг: Цена: 19140.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the Second International Challenge on Kidney and Kidney Tumor Segmentation, KiTS 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 21 contributions presented were carefully reviewed and selected from 29 submissions. This challenge aims to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy.
Автор: Andrearczyk Название: Head and Neck Tumor Segmentation and Outcome Prediction ISBN: 3031274199 ISBN-13(EAN): 9783031274190 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Описание: This book constitutes three challenges that were held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which took place in Singapore in September 2022. The peer-reviewed 10 papers included in this volume stem from the following three challenges: * Kidney Parsing Challenge 2022: Multi-Structure Segmentation for Renal Cancer Treatment (KiPA 2022) * The 2022 Correction of Brain Shift with Intra-Operative Ultrasound-Segmentation Challenge (CuRIOUS-SEG 2022) * The 2022 Mediastinal Lesion Analysis Challenge (MELA 2022)
Название: Medical Image Registration ISBN: 036739720X ISBN-13(EAN): 9780367397203 Издательство: Taylor&Francis Рейтинг: Цена: 10104.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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 provides the first comprehensive coverage of this emerging field.
This monograph details the theory, technology, and practical implementations in a variety of medical settings. International experts thoroughly explain why image registration is important, describe its applications in a nonmathematical way, and include rigorous analysis for those who plan to implement algorithms themselves. It is accessible and informative to those new to the field, yet it provides in-depth treatment for the expert. With its practical examples, extensive illustrations, and comprehensible approach, Medical Image Registration is a must have guide for medical physicists, clinicians, and researchers.
Описание: Addresses major techniques regarding image processing as a tool for disease identification and diagnosis, as well as treatment recommendation. An essential addition to the reference material available in the field of medicine, this timely publication covers a range of applied research on data mining, image processing, computational simulation, data visualization, and image retrieval.
Описание: Short compute times are crucial for timely diagnostics in biomedical applications, but lead to a high demand in computing for new and improved imaging techniques. In this book, reconfigurable computing with FPGAs is discussed as an alternative to multi-core processing and graphics card accelerators. Instead of adjusting the application to the hardware, FPGAs allow the hardware to also be adjusted to the problem. Acceleration of Biomedical Image Processing with Dataflow on FPGAs covers the transformation of image processing algorithms towards a system of deep pipelines that can be executed with very high parallelism. The transformation process is discussed from initial design decisions to working implementations. Two example applications from stochastic localization microscopy and electron tomography illustrate the approach further.
Topics discussed in the book include: - Reconfigurable hardware - Dataflow computing - Image processing - Application acceleration
Автор: Jo?o Manuel R. S. Tavares; Renato Natal Jorge Название: Developments in Medical Image Processing and Computational Vision ISBN: 331913406X ISBN-13(EAN): 9783319134062 Издательство: Springer Рейтинг: Цена: 19591.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Preface.- On the evaluation of automated MRI brain segmentations: technical and conceptual tools, by Elisabetta Binaghi, Valentina Pedoia, Desiree Lattanzi, Emanuele Monti, Sergio Balbi, Renzo Minotto.- Analysis of the retinal nerve fiber layer texture related to the thickness measured by optical coherence tomography, by J. Odstrcilik, R. Kolar, R.P. Tornow, A. Budai, J. Jan, P. Mackova, M. Vodakova.- Continuum mechanics meets echocardiographic imaging: investigation on the principal strain lines in human left ventricle, by A. Evangelista, S. Gabriele, P. Nardinocchi, P. Piras, P.E. Puddu, L. Teresi, C. Torromeo, V. Varano.- A GPU accelerated algorithm for blood detection in wireless capsule endoscopy images, by Sunil Kumar, Isabel N. Figueiredo, Carlos Graзa, Gabriel Falcгo.- Automated image mining in fMRI Reports: a meta-research study, by N. Gonзalves, G. Vranou, R. Vigбrio.- Visual pattern recognition framework based on the best rank tensor decomposition, by B. Cyganek.- Tracking red blood cells flowing through a microchannel with a hyperbolic contraction: an automatic method, by B. Taboada, F. C. Monteiro, R. Lima.- A 3D computed tomography based tool for orthopedic surgery planning, by Joгo Ribeiro, Victor Alves, Sara Silva, Jaime Campos.- Preoperative planning of surgical treatment with the use of 3D visualization and finite element method, by Wolański Wojciech, Gzik-Zroska Bożena, Kawlewska Edyta, Gzik Marek, Dzielicki Jуzef, Larysz Dawid, Rudnik Adam.- Pretreatment and reconstruction of three-dimensional images applied in a locking reconstruction plate for a structural analysis with FEA, by Joгo Paulo O. Freitas, Edson A. Capello de Sousa, Cesar R. Foschini, Rogerio R. Santos, Sheila C. Rahal.- Tortuosity influence on the trabecular bone elasticity and mechanical competence, by Waldir Leite Roque, Angel Alberich-Bayarri.- Influence of beam hardening artifact in bone interface contact evaluation by 3D X-ray microtomography, by I. Lima, M. Marquezan, M. M. G. Souza, E. F. Sant'Anna, R. T. Lopes.- Anisotropy estimation of trabecular bone in gray-scale: comparison between cone beam and micro computed tomography data, by Rodrigo Moreno, Magnus Borga, Eva Klintstrцm, Torkel Brismar, Цrjan Smedby.- Fractured bone identification from CT images, fragment separation and fracture zone detection, by Fйlix Paulano, Juan J. Jimйnez, Rubйn Pulido.- On evolutionary integral models for image restoration, by E. Cuesta, A. Durбn, M. Kirane.- Colour image quantisation using KM and KHM clustering techniques with outlier-based initialization, by Henryk Palus, Mariusz Frackiewicz.- A study of a firefly meta-heuristics for multithreshold image segmentation, by H. Erdmann, G. Wachs-Lopes, C. Gallгo, M. P. Ribeiro, P. S. Rodrigues.- Visual-inertial 2D feature tracking based on an affine photometric model, by Dominik Aufderheide, Gerard Edwards and Werner Krybus.- Inferring heading direction from silhouettes, by Amina Bensebaa, Slimane Larabi, Neil M. Robertson.- A fast and accurate algorithm for detecting and tracking moving hand gestures, by Walter C. S. S. Simхes, Ricardo da S. Barboza, Vicente F. de Lucena Jr., Rafael D. Lins.- Hand gesture recognition system based in computer vision and machine learning, by Paulo Trigueiros, Fernando Ribeiro, Luнs Paulo Reis.- 3D Scanning using RGBD imaging devices: a survey, by Eduardo E. Hitomi, Jorge V. L. Silva, Guilherme C. S. Ruppert.
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