The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.*The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging - others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.

The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.*The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging - others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.

The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.*The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging - others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.

The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.*The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging - others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.

This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021, held on September 27, 2021, in conjunction with MICCAI 2021. The workshop was held virtually due to the COVID-19 pandemic. The 17 papers presented in this book were carefully reviewed and selected from 27 submissions. They were organized in topical sections named: computational anatomy and brain networks and time series.

Over the past few decades there have been major advances in computed tomography (CT) to improve the performance of cardiac imaging. Thanks to the improved scanning speed, power boost tubes, and increased-width detectors, the latest CT technology delivers greater coverage, better spatial and temporal resolution, and functional information on cardiac diseases. Focusing on cardiac CT imaging, this book offers case-based information on cardiac diseases, presents the current technical status, and highlights applications, helping readers systematically understand how cardiac CTs are performed and interpreted in clinical practice. Divided into six chapters, it broadly discusses the characteristics of CT imaging and its applications to coronary artery disease (CAD); non-atherosclerotic coronary artery disease; congenital heart disease; cardiac neoplasms; cardiomyopathy and aortic diseases.

Covering the entire spectrum of this fast-changing field, Diagnostic Imaging: Chest, third edition, is an invaluable resource for general radiologists, thoracic radiologists, and trainees-anyone who requires an easily accessible, highly visual reference on today's thoracic imaging. Drs. Melissa L. Rosado-de-Christenson, Santiago Martinez-Jimenez, and their team of highly regarded experts provide up-to-date information on recent advances in technology and the understanding of chest diseases to help you make informed decisions at the point of care. The text is lavishly illustrated, delineated, and referenced, making it a useful learning tool as well as a handy reference for daily practice. Serves as a one-stop resource for key concepts and information on chest imaging, including a wealth of new material and content updates throughout Features more than 2,800 illustrations (full-color drawings, clinical and histologic photographs, and gross pathology images) as well as video clips demonstrating the diaphragmatic paralysis positive sniff test, virtual bronchoscopy fly-through, and more Features updates from cover to cover including new information on pulmonary manifestations of coronavirus infection/COVID-19 and numerous new chapters throughout Reflects updates in terminology and imaging findings of common neoplastic disorders (including primary lung cancer and lymphoma), and novel imaging findings of inhalational lung diseases, including those related to vaping Covers common thoracic malignancies and chest diseases with details on the latest knowledge in the field, including lung screening with low-dose chest CT, approach to the patient with incidentally discovered lung nodules, and updates on the imaging manifestations and management recommendations for common pulmonary infections Uses bulleted, succinct text and highly templated chapters for quick comprehension of essential information at the point of care Enhanced eBook version included with purchase. Your enhanced eBook allows you to access all of the text, figures, and references from the book on a variety of devices

This book constitutes the proceedings of the 27th International Conference on Information Processing in Medical Imaging, IPMI 2021, which was held online during June 28-30, 2021. The conference was originally planned to take place in Bornholm, Denmark, but changed to a virtual format due to the COVID-19 pandemic. The 59 full papers presented in this volume were carefully reviewed and selected from 200 submissions. They were organized in topical sections as follows: registration; causal models and interpretability; generative modelling; shape; brain connectivity; representation learning; segmentation; sequential modelling; learning with few or low quality labels; uncertainty quantification and generative modelling; and deep learning.

The fourth edition of Atlas of the Human Brain presents the anatomy of the brain at macroscopic and microscopic levels, featuring different aspects of brain morphology and topography. This greatly enlarged new edition provides the most detailed and accurate delineations of brain structure available. It includes features which assist in the new fields of neuroscience - functional imaging, resting state imaging and tractography. Atlas of the Human Brain is an essential guide to those working with human brain imaging or attempting to relate their observations on experimental animals to humans. Totally new in this edition is the inclusion of Nissl plates with delineation of cortical areas (Brodmann's areas), the first time that these areas have been presented in serial histological sections.

This textbook, intended for advanced undergraduate and graduate students, is an introduction to the physical and mathematical principles used in clinical medical imaging. The first two chapters introduce basic concepts and useful terms used in medical imaging and the tools implemented in image reconstruction, while the following chapters cover an array of topics such as physics of x-rays and their implementation in planar and computed tomography (CT) imaging; nuclear medicine imaging and the methods of forming functional planar and single photon emission computed tomography (SPECT) images and Clinical imaging using positron emitters as radiotracers. The book also discusses the principles of MRI pulse sequencing and signal generation, gradient fields, and the methodologies implemented for image formation, form flow imaging and magnetic resonance angiography and the basic physics of acoustic waves, the different acquisition modes used in medical ultrasound, and the methodologies implemented for image formation and flow imaging using the Doppler Effect. By the end of the book, readers will know what is expected from a medical image, will comprehend the issues involved in producing and assessing the quality of a medical image, will be able to conceptually implement this knowledge in the development of a new imaging modality, and will be able to write basic algorithms for image reconstruction. Knowledge of calculus, linear algebra, regular and partial differential equations, and a familiarity with the Fourier transform and it applications is expected, along with fluency with computer programming. The book contains exercises, homework problems, and sample exam questions that are exemplary of the main concepts and formulae students would encounter in a clinical setting.

Cardiac implantable device (CIED) infections are a key issue since they have serious consequences in terms of morbidity, mortality and associated costs for healthcare systems. Such infections are highly complex, which has implications for prevention (several factors underlie its development), diagnosis (requiring patient specific integration of several elements), acute treatment (choice of the antibiotic, lead extraction procedure), as well as long-term management (CIED re-implantation strategy and follow-up). Dealing with CIED infections requires appropriate organization of healthcare provision, with the creation of integrated hub-spoke referrals, and it has prompted many of the recent developments in CIED technologies: leadless pacemakers, subcutaneous defibrillators, antibacterial envelops and lead extraction devices. This book systematically examines all aspects of this multifaceted condition, discussing each clinical and organizational issue in a separate self-contained chapter. It starts by reviewing the epidemiology/pathophysiology of CIED infections, and then addresses acute and long-term management. Offering a comprehensive overview of the prevention of infections, considering both current and future perspectives, the book also provides practical suggestions along with recommendations from the literature in the field. This is the first book entirely focused on CIED infections. Written by an international team of leading experts it will appeal to broad audience, including cardiologists, electrophysiologists, infectious disease specialists, internists, healthcare professionals, health service managers and providers, researchers and those working in industries involved in the field.

A comprehensive, systemically organised 'film viewing' book designed specifically for FRCR candidates but equally useful to all trainee and junior radiologists as an 'aide memoire' and to medical undergraduates and MRCP candidates as a useful means of self-testing their level of knowledge. It is fully illustrated with high-quality, clearly labeled black and white illustrations throughout and the text closely mirrors the way that questions are asked in the FRCR film viewing examination, so as to provide as real an examination experience as possible. Each section of the book contains 20 representative questions, and model answers, providing 200 cases altogether. First published in 2000.

This book constitutes the First Cerebral Aneurysm Detection Challenge, CADA 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in October 2020. The conference was planned to take place in Lima, Peru, and took place virtually due to the COVID-19 pandemic. The 9 regular papers presented in this volume, together with an overview and one introduction paper, were carefully reviewed and selected for inclusion in the book. The papers were organized in topical sections as follows: cerebral aneurysm detection; cerebral aneurysm segmentation; and cerebral aneurysm rupture risk estimation.

The three-volume set LNCS 6891, 6892 and 6893 constitutes the refereed proceedings of the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011, held in Toronto, Canada, in September 2011. Based on rigorous peer reviews, the program committee carefully selected 251 revised papers from 819 submissions for presentation in three volumes. The second volume includes 83 papers organized in topical sections on diffusion weighted imaging, fMRI, statistical analysis and shape modeling, and registration.

This two-volume set LNCS 12658 and 12659 constitutes the thoroughly refereed proceedings of the 6th International MICCAI Brainlesion Workshop, BrainLes 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in Lima, Peru, in October 2020.* The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (16 selected papers from 21 submissions); brain tumor image segmentation (69 selected papers from 75 submissions); and computational precision medicine: radiology-pathology challenge on brain tumor classification (6 selected papers from 6 submissions). *The workshop and challenges were held virtually.

This two-volume set LNCS 12658 and 12659 constitutes the thoroughly refereed proceedings of the 6th International MICCAI Brainlesion Workshop, BrainLes 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in Lima, Peru, in October 2020.* The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (16 selected papers from 21 submissions); brain tumor image segmentation (69 selected papers from 75 submissions); and computational precision medicine: radiology-pathology challenge on brain tumor classification (6 selected papers from 6 submissions). *The workshop and challenges were held virtually.

This book constitutes the refereed proceedings of the 5th International Workshop and Challenge on Computational Methods and Clinical Applications for Musculoskeletal Imaging, MSKI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 13 workshop papers were carefully reviewed and selected for inclusion in this volume. Topics of interest include all major aspects of musculoskeletal imaging, for example: clinical applications of musculoskeletal computational imaging; computer-aided detection and diagnosis of conditions of the bones, muscles and joints; image-guided musculoskeletal surgery and interventions; image-based assessment and monitoring of surgical and pharmacological treatment; segmentation, registration, detection, localization and visualization of the musculoskeletal anatomy; statistical and geometrical modeling of the musculoskeletal shape and appearance; image-based microstructural characterization of musculoskeletal tissue; novel techniques for musculoskeletal imaging.

The scanning tunnelling microscope (STM) was invented by Binnig and Rohrer and received a Nobel Prize of Physics in 1986. Together with the atomic force microscope (AFM), it provides non-destructive atomic and subatomic resolution on surfaces. Especially, in recent years, internal details of atomic and molecular wavefunctions are observed and mapped with negligible disturbance. Since the publication of its first edition, this book has been the standard reference book and a graduate-level textbook educating several generations of nano-scientists. In Aug. 1992, the co-inventor of STM, Nobelist Heinrich Rohrer recommended: "The Introduction to Scanning tunnelling Microscopy by C.J. Chen provides a good introduction to the field for newcomers and it also contains valuable material and hints for the experts". For the second edition, a 2017 book review published in the Journal of Applied Crystallography said "Introduction to Scanning tunnelling Microscopy is an excellent book that can serve as a standard introduction for everyone that starts working with scanning probe microscopes, and a useful reference book for those more advanced in the field". The third edition is a thoroughly updated and improved version of the recognized "Bible" of the field. Additions to the third edition include: theory, method, results, and interpretations of the non-destructive observation and mapping of atomic and molecular wavefunctions; elementary theory and new verifications of equivalence of chemical bond interaction and tunnelling; scanning tunnelling spectroscopy of high Tc superconductors; imaging of self-assembled organic molecules on the solid-liquid interfaces. Some key derivations are rewritten using mathematics at an undergraduate level to make it pedagogically sound.

Preoperative imaging is increasingly being adopted for preoperative planning in plastic and reconstructive surgery. Accurate preoperative analysis can reduce the length of operations and maximize surgical design and dissection techniques. Imaging for Plastic Surgery covers the techniques, applications, and potentialities of medical imaging technology in plastic and reconstructive surgery. Presenting state-of-the-art research on evolving imaging modalities, this cutting-edge text: Provides a practical introduction to imaging modalities that can be used during preoperative planning Addresses imaging principles of the face, head, neck, breast, trunk, and extremities Identifies the strengths and weaknesses of all available imaging modalities Demonstrates the added value of imaging in different clinical scenarios Comprised of contributions from world-class experts in the field, Imaging for Plastic Surgery is an essential imaging resource for surgeons, radiologists, and patient care professionals.

This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

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.

The book discusses the impact of machine learning and computational intelligent algorithms on medical image data processing, and introduces the latest trends in machine learning technologies and computational intelligence for intelligent medical image analysis. The topics covered include automated region of interest detection of magnetic resonance images based on center of gravity; brain tumor detection through low-level features detection; automatic MRI image segmentation for brain tumor detection using the multi-level sigmoid activation function; and computer-aided detection of mammographic lesions using convolutional neural networks.

This book constitutes the refereed proceedings of the 24th Conference on Medical Image Understanding and Analysis, MIUA 2020, held in July 2020. Due to COVID-19 pandemic the conference was held virtually. The 29 full papers and 5 short papers presented were carefully reviewed and selected from 70 submissions. They were organized according to following topical sections: image segmentation; image registration, reconstruction and enhancement; radiomics, predictive models, and quantitative imaging biomarkers; ocular imaging analysis; biomedical simulation and modelling.

Addressing the basic concepts of radiological physics and radiation protection, together with a structured approach to image interpretation, Radiology at a Glance is the perfect guide for medical students, junior doctors and radiologists. Covering the radiology of plain films, fluoroscopy, CT, MRI, intervention, nuclear medicine, and mammography, this edition has been fully updated to reflect advances in the field and now contains new spreads on cardiac, breast and bowel imaging, as well as further information on interventional radiology. Radiology at a Glance: * Assumes no prior knowledge of radiology * Addresses both theory and clinical practice through theoretical and case-based chapters * Provides structured help in assessing which radiological procedures are most appropriate for specific clinical problems * Includes increased image clarity Supported by 'classic cases' chapters in each section, and presented in a clear and concise format, Radiology at a Glance is easily accessible whether on the ward or as a quick revision guide.

This volume provides a thought-provoking, interdisciplinary perspective on the empirical and analytic study of body composition and the techniques used for measuring body components, such as fat, water, muscle, and bone mass and density. The nature of regional differences, developmental changes, pathological abnormalities, and the impact of heredity and environment in shaping body composition are discussed in the context of human evolution. For instance, the author looks at how growth, aging, and exposure to heat, cold, excess or lack of nourishment modify normal body composition.