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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 will provide readers with a good overview of some of the most recent advances in the field of detector technology for gamma-ray imaging, especially as it pertains to new applications. There will be a good mixture of general chapters in both technology and applications in medical imaging and industrial testing. The book will have an in-depth review of the research topics from world-leading specialists in the field. The conversion of the gamma-ray signal into analog/digital value will be covered in some chapters. Some would also provide a review of CMOS chips for gamma-ray image sensors.
This book offers readers an overview of some of the most recent advances in the field of technology for X-ray medical imaging. Coverage includes both technology and applications in SPECT, PET and CT, with an in-depth review of the research topics from leading specialists in the field. Coverage includes conversion of the X-ray signal into analogue/digital value, as well as a review of CMOS chips for X-ray image sensors. Emphasis is on high-Z materials like CdTe, CZT and GaAs, since they offer the best implementation possibilities for direct conversion X-ray detectors. The discussion includes material challenges, detector operation physics and technology and readout integrated circuits required to detect signals processes by high-Z sensors. Authors contrast these emerging technologies with more established ones based on scintillator materials. This book is an excellent reference for people already working in the field as well as for people wishing to enter it.
Image synthesis across and within medical imaging modalities is an active area of research with broad applications in radiology and radiation oncology. This book covers the principles and methods of medical image synthesis, along with state-of-the-art research. First, various traditional non-learning-based, traditional machine-learning-based, and recent deep-learning-based medical image synthesis methods are reviewed. Second, specific applications of different inter- and intra-modality image synthesis tasks and of synthetic image-aided segmentation and registration are introduced and summarized, listing and highlighting the proposed methods, study designs, and reported performances with the related clinical applications of representative studies. Third, the clinical usages of medical image synthesis, such as treatment planning and image-guided adaptive radiotherapy, are discussed. Last, the limitations and current challenges of various medical synthesis applications are explored, along with future trends and potential solutions to solve these difficulties. The benefits of medical image synthesis have sparked growing interest in a number of advanced clinical applications, such as magnetic resonance imaging (MRI)-only radiation therapy treatment planning and positron emission tomography (PET)/MRI scanning. This book will be a comprehensive and exciting resource for undergraduates, graduates, researchers, and practitioners.
The advances in semiconductor detectors, scintillators, photodetectors such as silicon photomultipliers (SiPM), and readout electronics have experienced tremendous growth in recent years in terms of basic technologies and a variety of applications. The second edition of Radiation Detection Systems presents variety of radiation detection systems, giving readers a broad view of the state-of-the-art in the design of detectors, front-end electronics, and systems offering optimized choices of the detection tools for a particular application. The new edition has been divided into two volumes. This volume on Medical Imaging, Industrial Testing, and Security Applications presents specific applications of the detection systems in medical imaging, industrial testing, and security applications. These newely developed technologies play a vital role in the detection, diagnosis, and treatment of major human diseases. Featuring contributions from leading experts and pioneers in their respective fields, this book: Describes new advances in development of detection systems based on CdZnTe (CZT) and CdTe detectors utilizing a direct conversion of radiation to electric signals Reports a recent progress in technologies and performance of SiPM used for reading the light from scintillators Explores exciting new application opportunities created by development of the cutting-edge detection technologies in X-ray spectroscopy, computed tomography (CT), bone dosimetry, and nuclear medicine (PET, SPECT) Considers the future use of photon-counting detectors in clinical CT scanners providing K-edge imaging to reduce the amount of contrast agents and ultimately offering both an anatomical and a functional information Describes, uses of radiation detection systems in security applications such as luggage scanning, dirty bomb detection, and border control With its combined coverage of new materials and innovative new system approaches, as well as a succinct overview of recent developments, this book is an invaluable tool for any engineer, professional, or student working in electronics or an associated field. Readers can refer to the other volume, Sensor Materials, Systems, Technology, and Characterization Measurements, which puts emphasis on sensor materials, detector structures, front electronics technology, and their designs and system optimization for different applications.
This book is dedicated to addressing the major challenges in fighting COVID-19 using artificial intelligence (AI) and machine learning (ML) – from cost and complexity to availability and accuracy. The aim of this book is to focus on both the design and implementation of AI-based approaches in proposed COVID-19 solutions that are enabled and supported by sensor networks, cloud computing, and 5G and beyond. This book presents research that contributes to the application of ML techniques to the problem of computer communication-assisted diagnosis of COVID-19 and similar diseases. The authors present the latest theoretical developments, real-world applications, and future perspectives on this topic. This book brings together a broad multidisciplinary community, aiming to integrate ideas, theories, models, and techniques from across different disciplines on intelligent solutions/systems, and to inform how cognitive systems in Next Generation Networks (NGN) should be designed, developed, and evaluated while exchanging and processing critical health information. Targeted readers are from varying disciplines who are interested in implementing the smart planet/environments vision via wireless/wired enabling technologies.
Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.