This book includes high-quality papers presented at the Second International Symposium on Computer Vision and Machine Intelligence in Medical Image Analysis (ISCMM 2021), organized by Computer Applications Department, SMIT in collaboration with Department of Pathology, SMIMS, Sikkim, India, and funded by Indian Council of Medical Research, during 11 - 12 November 2021. It discusses common research problems and challenges in medical image analysis, such as deep learning methods. It also discusses how these theories can be applied to a broad range of application areas, including lung and chest x-ray, breast CAD, microscopy and pathology. The studies included mainly focus on the detection of events from biomedical signals.

This book comprises a collection of papers presented at the International Workshop on New Approaches for Multidimensional Signal Processing (NAMSP 2021), held at Technical University of Sofia, Sofia, Bulgaria, during 08-10 July 2021. The book covers research papers in the field of N-dimensional multicomponent image processing, multidimensional image representation and super-resolution, 3D image processing and reconstruction, MD computer vision systems, multidimensional multimedia systems, neural networks for MD image processing, data-based MD image retrieval and knowledge data mining, watermarking, hiding and encryption of MD images, MD image processing in robot systems, tensor-based data processing, 3D and multi-view visualization, forensic analysis systems for MD images and many more.

Digital Health: Exploring Use and Integration of Wearables is the first book to show how and why engineering theory is used to solve real-world clinical applications, considering the knowledge and lessons gathered during many international projects. This book provides a pragmatic A to Z guide on the design, deployment and use of wearable technologies for laboratory and remote patient assessment, aligning the shared interests of diverse professions to meet with a common goal of translating engineering theory to modern clinical practice. It offers multidisciplinary experiences to guide engineers where no clinically advice and expertise may be available. Entering the domain of wearables in healthcare is notoriously difficult as projects and ideas often fail to deliver due to the lack of clinical understanding, i.e., what do healthcare professionals and patients really need? This book provides engineers and computer scientists with the clinical guidance to ensure their novel work successfully translates to inform real-world clinical diagnosis, treatment and management.

This book covers innovative breakthroughs in additive manufacturing processes used for biomedical engineering. More and more, 3D printing is selected over traditional manufacturing processes, especially for complex designs, because of the many advantages such as fewer restrictions, better production cost savings, higher quality control, and accuracy. Current challenges and opportunities regarding material, design, cost savings, and efficiency are covered along with an outline of the most recent fabrication methods used for converting biomaterials into integrated structures that can fit best in anatomy while still obtaining the necessary architecture, mechanical reliability, biocompatibility, and anti-bacterial characteristics needed. Additional chapters will also focus on selected areas of applications such as bionics, affordable prostheses, implants, medical devices, rapid tooling, and drug delivery. Additive Manufacturing Processes in Biomedical Engineering: Advanced Fabrication Methods and Rapid Tooling Techniques acts as a first-hand reference for commercial manufacturing organizations which are mimicking tissue organs by using additive manufacturing techniques. By capturing the current trends of today's manufacturing practices this book becomes a one-stop resource for manufacturing professionals, engineers in related disciplines, and academic researchers.

This book examines the role of nanobiosensors in point-of-care applications for personalized healthcare and management. It begins by discussing various biomaterials that are used for the development of biosensors in medical diagnostics, and reviews advances in their fabrication and the miniaturization of biosensor devices for lab-on-chip analysis. In turn, it explores the rapidly evolving applications of nanomaterials in the context of biomaterial diagnostics. The book also explores the immense potential of biosensors in medical diagnostics, where they are increasingly being used to detect a wide range of biomolecules and biomarkers. In closing, it discusses the current challenges and outlines the future role of nanobiosensors in the development of next-generation point-of-care applications.

Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images.

Principles and Labs for Deep Learning provides the knowledge and techniques needed to help readers design and develop deep learning models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code examples, and analyzed through the visualization of results. The structured methods and labs provided by Dr. Huang and Dr. Le enable readers to become proficient in TensorFlow to build deep Convolutional Neural Networks (CNNs) through custom APIs, high-level Keras APIs, Keras Applications, and TensorFlow Hub. Each chapter has one corresponding Lab with step-by-step instruction to help the reader practice and accomplish a specific learning outcome. Deep Learning has been successfully applied in diverse fields such as computer vision, audio processing, robotics, natural language processing, bioinformatics and chemistry. Because of the huge scope of knowledge in Deep Learning, a lot of time is required to understand and deploy useful, working applications, hence the importance of this new resource. Both theory lessons and experiments are included in each chapter to introduce the techniques and provide source code examples to practice using them. All Labs for this book are placed on GitHub to facilitate the download. The book is written based on the assumption that the reader knows basic Python for programming and basic Machine Learning.

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians.

Biomedical Engineering Tools for Management of Patients with COVID-19 presents biomedical engineering tools under research (and in development) that can be used for the management of COVID-19 patients, along with BME tools in the global environment that curtail and prevent the spread of the virus. BME tools covered in the book include new disinfectants and sterilization equipment, testing devices for rapid and accurate COVID-19 diagnosis, Internet of Things applications in COVID-19 hospitals, analytics, Data Science and statistical modeling applied to COVID-19 tracking, Smart City instruments and applications, and more. Later sections discuss smart tools in telemedicine and e-health. Biomedical engineering tools can provide engineers, computer scientists, clinicians and other policymakers with solutions for managing patient treatment, applying data analysis techniques, and applying tools to help the general population curtail spread of the virus.

Information Physics: Physics-Information and Quantum Analogies for Complex Modeling presents a new theory of complex systems that uses analogy across various aspects of physics, including electronics, magnetic circuits and quantum mechanics. The book explains the quantum approach to system theory that can be understood as an extension of classical system models. The main idea is that in many complex systems there are incomplete pieces of overlapping information that must be strung together to find the most consistent model. This incomplete information can be understood as a set of non-exclusive observer results. Because they are non-exclusive, each observer registers different pictures of reality.

This detailed volume focuses on the CRISPR-associated guide RNA and how it can be designed, modified, and validated for a broad repertoire of purposes. Beginning with a section on computational design of target-specific guide RNAs, the book continues by covering chemical modifications to alter guide RNA stability, specificity, and efficiency, as well as to create inducible guide RNAs, append additional functional domains, and express guide RNAs in a conditional manner. It concludes with methods for measuring off-target guide RNA activity. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and essential, CRISPR Guide RNA Design: Methods and Protocols provides a comprehensive pipeline for guide RNA design and aims to be an invaluable resource in applying this powerful technology to basic research and therapeutic applications.

This book highlights the latest advances in functional micro/nano imaging probes and their applications for biomedical imaging and therapy. Given the rapid emergence of transdisciplinary research and applications in materials, chemical probes and translational medicine in recent years, scientists in these areas are expected to keep up to date on the latest technologies and advances to promote comprehensive innovations. Addressing this need, the book presents recently introduced features, emerging techniques, and new strategies, complemented by detailed illustrations. Covering the status quo and offering an outlook on the future, it benefits all readers with an interest in functional materials, especially micro/nano imaging materials for biomedical imaging applications, providing them with both vital updates and inspiration for their own research.

This volume is the third in a series entitled" Advanced Methods of Physiological System Modeling" and the fifth in a series of research volumes published by Plenum under the sponsorship of the Biomedical Simulations Resource (BMSR) at the Uni versity of Southern California in the context of dissemination activities supported by the Biomedical Research Technology Program of the National Center for Research Resources at the National Institutes of Health under Grant No. P41 RR-OI861. These volumes are edited by BMSR principal scientists and report on recent research de velopments in the area of physiological systems modeling, as well as on advanced methods for analysis of physiological signals and data. As in the previous two volumes of this series, the work reported herein is con cerned with the development of advanced modeling methodologies and their novel application to problems of biomedical interest, with emphasis on nonlinear aspects of physiological function. The term "advanced methodologies" is used to indicate that the scope of this work extends beyond the ordinary type of analysis, which is confined traditionally to the linear domain. As the importance of nonlinearities in understanding the complex mechanisms of physiological function is increasingly recognized, the need for effective and practical modeling methodologies that address the issue of nonlinear dynamics in life sciences becomes more and more pressing."

This book comprises select peer-reviewed proceedings of the medical challenge - C-NMC challenge: Classification of normal versus malignant cells in B-ALL white blood cancer microscopic images. The challenge was run as part of the IEEE International Symposium on Biomedical Imaging (IEEE ISBI) 2019 held at Venice, Italy in April 2019. Cell classification via image processing has recently gained interest from the point of view of building computer-assisted diagnostic tools for blood disorders such as leukaemia. In order to arrive at a conclusive decision on disease diagnosis and degree of progression, it is very important to identify malignant cells with high accuracy. Computer-assisted tools can be very helpful in automating the process of cell segmentation and identification because morphologically both cell types appear similar. This particular challenge was run on a curated data set of more than 14000 cell images of very high quality. More than 200 international teams participated in the challenge. This book covers various solutions using machine learning and deep learning approaches. The book will prove useful for academics, researchers, and professionals interested in building low-cost automated diagnostic tools for cancer diagnosis and treatment.

Thermal Ablation Therapy: Theory and Simulation includes detailed theoretical and technical concepts of thermal ablation therapy in different body organs. Concepts of ablation technology based on different thermal ablation methods are introduced, along with changes in the tissues' mechanical properties due to thermal denaturation. The book emphasizes the mathematical and engineering concepts of RF and MW energy propagation through tissues and where high heating rates produced by MW systems can overcome the heat-sink effects from nearby vessels. The design and tuning of the MW antennas to deliver energy efficiently to specific organ systems such as the liver or lung is also covered. Other sections cover the computational modeling of radiofrequency ablation and microwave ablation procedures for developing and implementing new efficient ablation in clinical systems, numerical simulations for different scenarios of different organs with different size using RF and MW ablation systems with different antennas'/probes design and configurations, and numerical techniques for temperature profile in tissues.

Autonomous Robot-Aided Optical Manipulation for Biological Cells gives a systematically and almost self-contained description of the many facets of modeling, sensing, and control techniques or experimentally exploring emerging trends in optical manipulation of biological cell in micro/nanorobotics systems. To achieve biomedical applications, reliability design, modeling, and precision control are vitally important for the development of engineering systems. With the advances in modeling, sensing, and control techniques, it is opportunistic to exploit them for the benefit of reliability design, actuation, and precision control of micro/nanomanipulation systems to expanding the applications of robot at the micro and nano scales, especially in biomedical engineering. This book presents new techniques in reliability modeling and advanced control of robot-aided optical manipulation of biological cells systems. The book will be beneficial to the researchers within robotics, mechatronics, biomedical engineering, and automatic control society, including both academic and industrial parts.

This book introduces a variety of well-proven and newly developed nature-inspired optimization algorithms solving a wide range of real-life biomedical and healthcare problems. Few solo and hybrid approaches are demonstrated in a lucid manner for the effective integration and finding solution for a large-scale complex healthcare problem. In the present bigdata-based computing scenario, nature-inspired optimization techniques present adaptive mechanisms that permit the understanding of complex data and altering environments. This book is a voluminous collection for the confront faced by the healthcare institutions and hospitals for practical analysis, storage, and data analysis. It explores the distinct nature-inspired optimization-based approaches that are able to handle more accurate outcomes for the current biomedical and healthcare problems. In addition to providing a state-of-the-art and advanced intelligent methods, it also enlightens an insight for solving diversified healthcare problems such as cancer and diabetes.

The vision of seamless human-robot interaction in our everyday life that allows for tight cooperation between human and robot has not become reality yet. However, the recent increase in technology maturity finally made it possible to realize systems of high integration, advanced sensorial capabilities and enhanced power to cross this barrier and merge living spaces of humans and robot workspaces to at least a certain extent. Together with the increasing industrial effort to realize first commercial service robotics products this makes it necessary to properly address one of the most fundamental questions of Human-Robot Interaction: How to ensure safety in human-robot coexistence? In this authoritative monograph, the essential question about the necessary requirements for a safe robot is addressed in depth and from various perspectives. The approach taken in this book focuses on the biomechanical level of injury assessment, addresses the physical evaluation of robot-human impacts, and isolates the major factors that cause human injuries. This assessment is the basis for the design and exploration of various measures to improve safety in human-robot interaction. They range from control schemes for collision detection, reflex reaction, and avoidance to the investigation of novel joint designs that equip robots with fundamentally new capabilities. By the depth of its analysis and exceptionally salient experimental work, this monograph offers one of the most comprehensive treatments of the safety challenge in the field.

Signal Processing in Medicine and Biology: Innovations in Big Data Processing provides an interdisciplinary look at state-of-the-art innovations in biomedical signal processing, especially as it applies to large data sets and machine learning. Chapters are presented with detailed mathematics and complete implementation specifics so that readers can completely master these techniques. The book presents tutorials and examples of successful applications and will appeal to a wide range of professionals, researchers, and students interested in applications of signal processing, medicine, and biology at the intersection between healthcare, engineering, and computer science.

This thesis advances the long-standing challenge of measuring oxidative stress and deciphering its underlying mechanisms, and also outlines the advantages and limitations of existing design strategies. It presents a range of approaches for the chemical synthesis of fluorescent probes that detect reversible changes in cellular oxidative stress. The ability to visualise cellular processes in real-time is crucial to understanding disease development and streamline treatment, and this can be achieved using fluorescent tools that can sense reversible disturbances in cellular environments during pathogenesis. The perturbations in cellular redox state are of particular current interest in medical research, since oxidative stress is implicated in the pathogenesis of a number of diseases. The book investigates different strategies used to achieve ratiometric fluorescence output of the reversible redox probes, which nullify concentration effects associated with intensity-based probes. It also describes suitable approaches to target these probes to specific cellular organelles, thereby enabling medical researchers to visualise sub-cellular oxidative stress levels, and addressing the typically poor uptake of chemical tools into biological studies. In total it reports on four new probes that are now being used by over twenty research groups around the globe, and two of which have been commercialised. The final chapters of this thesis demonstrate successful applications of the sensors in a variety of biological systems ranging from prokaryotes to mammalian cells and whole organisms. The results described clearly indicate the immense value of collaborative, cross-disciplinary research.

Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks.

This book covers the development of biotechnology based on carbon nanostructures, with a focus on nanotubes, addressing also fullerenes and amorphous carbons. The book is divided into 7 chapters, addressing tissue engineering, genetic engineering and therapy, as well as the environmental and health impacts of carbon nanostructures.

This book provides a comprehensive overview of the design, generation and characterization of minimal cell systems. Written by leading experts, it presents an in-depth analysis of the current issues and challenges in the field, including recent advances in the generation and characterization of reduced-genome strains generated from model organisms with relevance in biotechnology, and basic research such as Escherichia coli, Corynebacterium glutamicum and yeast. It also discusses methodologies, such as bottom-up and top-down genome minimization strategies, as well as novel analytical and experimental approaches to characterize and generate minimal cells. Lastly, it presents the latest research related to minimal cells of serveral microorganisms, e.g. Bacillus subtilis. The design of biological systems for biotechnological purposes employs strategies aimed at optimizing specific tasks. This approach is based on enhancing certain biological functions while reducing other capacities that are not required or that could be detrimental to the desired objective. A highly optimized cell factory would be expected to have only the capacity for reproduction and for performing the expected task. Such a hypothetical organism would be considered a minimal cell. At present, numerous research groups in academia and industry are exploring the theoretical and practical implications of constructing and using minimal cells and are providing valuable fundamental insights into the characteristics of minimal genomes, leading to an understanding of the essential gene set. In addition, research in this field is providing valuable information on the physiology of minimal cells and their utilization as a biological chassis to which useful biotechnological functions can be added.

Structural Biomaterials: Properties, Characteristics, and Selection serves as a single point of reference to digest current research and develop a deeper understanding in the field of biomaterials engineering. This book uses a materials-focused approach, allowing the reader to quickly access specific, detailed information on biomaterials characterization and selection. Relevant to a range of readers, this book provides holistic coverage of the broad categories of structural biomaterials currently available and used in medical applications, highlighting the property requirements for structural biomaterials, their biocompatibility performance and their safety regulation in key categories such as metals, ceramics and polymers. The materials science perspective of this text ensures the content is accessible even to those without an extensive background in applied medicine, positioning this text not just for students, but as an overview and reference for researchers, scientists and engineers entering the field from related materials science disciplines.

Handbook of Decision Support Systems for Neurological Disorders provides readers with complete coverage of advanced computer-aided diagnosis systems for neurological disorders. While computer-aided decision support systems for different medical imaging modalities are available, this is the first book to solely concentrate on decision support systems for neurological disorders. Due to the increase in the prevalence of diseases such as Alzheimer, Parkinson's and Dementia, this book will have significant importance in the medical field. Topics discussed include recent computational approaches, different types of neurological disorders, deep convolution neural networks, generative adversarial networks, auto encoders, recurrent neural networks, and modified/hybrid artificial neural networks.