Advertisement
| digital image analysis pathology: Whole Slide Imaging Anil V. Parwani, 2021-10-29 This book provides up-to-date and practical knowledge in all aspects of whole slide imaging (WSI) by experts in the field. This includes a historical perspective on the evolution of this technology, technical aspects of making a great whole slide image, the various applications of whole slide imaging and future applications using WSI for computer-aided diagnosis The goal is to provide practical knowledge and address knowledge gaps in this emerging field. This book is unique because it addresses an emerging area in pathology for which currently there is only limited information about the practical aspects of deploying this technology. For example, there are no established selection criteria for choosing new scanners and a knowledge base with the key information. The authors of the various chapters have years of real-world experience in selecting and implementing WSI solutions in various aspects of pathology practice. This text also discusses practical tips and pearls to address the selection of a WSI vendor, technology details, implementing this technology and provide an overview of its everyday uses in all areas of pathology. Chapters include important information on how to integrate digital slides with laboratory information system and how to streamline the “digital workflow” with the intent of saving time, saving money, reducing errors, improving efficiency and accuracy, and ultimately benefiting patient outcomes. Whole Slide Imaging: Current Applications and Future Directions is designed to present a comprehensive and state-of the-art approach to WSI within the broad area of digital pathology. It aims to give the readers a look at WSI with a deeper lens and also envision the future of pathology imaging as it pertains to WSI and associated digital innovations. |
| digital image analysis pathology: Artificial Intelligence and Machine Learning for Digital Pathology Andreas Holzinger, Randy Goebel, Michael Mengel, Heimo Müller, 2020-06-24 Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ‘‘fit-for-purpose’’ samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions. |
| digital image analysis pathology: Medical Image Processing for Improved Clinical Diagnosis Swarnambiga, A., 2018-08-31 In the medical field, there is a constant need to improve professionals’ abilities to provide prompt and accurate diagnoses. The use of image and pattern recognizing software may provide support to medical professionals and enhance their abilities to properly identify medical issues. Medical Image Processing for Improved Clinical Diagnosis provides emerging research exploring the theoretical and practical aspects of computer-based imaging and applications within healthcare and medicine. Featuring coverage on a broad range of topics such as biomedical imaging, pattern recognition, and medical diagnosis, this book is ideally designed for medical practitioners, students, researchers, and others in the medical and engineering fields seeking current research on the use of images to enhance the accuracy of medical prognosis. |
| digital image analysis pathology: Artificial Intelligence and Deep Learning in Pathology Stanley Cohen, 2020-06-02 Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. - Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. - Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. - Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more. |
| digital image analysis pathology: Digital Pathology Constantino Carlos Reyes-Aldasoro, Andrew Janowczyk, Mitko Veta, Peter Bankhead, Korsuk Sirinukunwattana, 2019-07-03 This book constitutes the refereed proceedings of the 15th European Congress on Digital Pathology, ECDP 2019, held in Warwick, UK in April 2019. The 21 full papers presented in this volume were carefully reviewed and selected from 30 submissions. The congress theme will be Accelerating Clinical Deployment, with a focus on computational pathology and leveraging the power of big data and artificial intelligence to bridge the gaps between research, development, and clinical uptake. |
| digital image analysis pathology: Haschek and Rousseaux's Handbook of Toxicologic Pathology, Volume 1: Principles and Practice of Toxicologic Pathology Wanda M. Haschek, Colin G. Rousseaux, Matthew A. Wallig, Brad Bolon, 2021-10-20 Haschek and Rousseaux's Handbook of Toxicologic Pathology, recognized by many as the most authoritative single source of information in the field of toxicologic pathology, has been extensively updated to continue its comprehensive and timely coverage. The fourth edition has been expanded to four separate volumes due to an explosion of information in this field requiring new and updated chapters. Completely revised with a number of new chapters, Volume 1, Principles and the Practice of Toxicologic Pathology, covers the practice of toxicologic pathology in three parts: Principles of Toxicologic Pathology, Methods in Toxicologic Pathology, and the Practice of Toxicologic Pathology. Other volumes in this work round out the depth and breadth of coverage.Volume 2 encompasses Toxicologic Pathology in Safety Assessment and Environmental Toxicologic Pathology. These two sections cover the application of toxicologic pathology in developing specific product classes, principles of data interpretation for safety assessment, and toxicologic pathology of major classes of environmental toxicants. Volumes 3 and 4 provide deep and broad treatment of Target Organ Toxicity, emphasizing the comparative and correlative aspects of normal biology and toxicant-induced dysfunction, principal methods for toxicologic pathology evaluation, and major mechanisms of toxicity. These volumes comprise the most authoritative reference on toxicologic pathology for pathologists, toxicologists, research scientists, and regulators studying and making decisions on drugs, biologics, medical devices, and other chemicals, including agrochemicals and environmental contaminants. Each volume is being published separately. - Provides new chapters on digital pathology, juvenile pathology, in vitro/in vivo correlation, big data technologies and in-depth discussion of timely topics in the area of toxicologic pathology - Offers high-quality and trusted content in a multi-contributed work written by leading international authorities in all areas of toxicologic pathology - Features hundreds of full-color images in both the print and electronic versions of the book to highlight difficult concepts with clear illustrations |
| digital image analysis pathology: Basic and Advanced Laboratory Techniques in Histopathology and Cytology Pranab Dey, 2023-01-01 The second edition of this well-received book provides detailed information on the basic and advanced laboratory techniques in histopathology and cytology. It offers clear guidance on the principles and techniques of routine and special laboratory techniques. It also covers advanced laboratory techniques such as immunocytochemistry, flow cytometry, liquid-based cytology, polymerase chain reactions, tissue microarray, molecular technology, etc. The book's second edition covers several important recent topics with many new chapters, such as liquid biopsy, artificial neural network, digital pathology, and next-generation sequencing. Each chapter elucidates basic principle, practical methods, troubleshooting, and clinical applications of the technique. It includes multiple colored line drawings, microphotographs, and tables to illustrate each technique. The book is a helpful guide to the post-graduate students and fellows in pathology, practicing pathologists, as well as laboratory technicians, and research students. |
| digital image analysis pathology: Modern Techniques in Cytopathology M.M. Bui, L. Pantanowitz, 2020-01-13 In the era of precision medicine, physicians are increasingly in need of more definitive diagnostic, prognostic, and predictive information derived from small biopsy specimens such as cytology samples in order to guide effective patient care. Cytopathology is well poised to meet this challenge. Whilst the traditional cytomorphologic component of cytology practice is still valid, enormous advances have been made in the field of cytopathology thanks to transformative technology and innovative individuals that have augmented the cytologists' ability to meet the demands of modern medicine. The purpose of this book is to describe, illustrate, and review many of the most recent developments regarding modern techniques employed in cytopathology. This latest monograph is intended for all cytologists including cytopathologists, cytotechnologists, cytology lab assistants, trainees, research scientists, and anyone who is interested in the field of cytopathology. We have invited pioneering experts in their respective fields to author these chapters. This book is not only the culmination of their groundbreaking work and effort but also presents a critical review of the current literature. We have attempted to provide readers with an informative and comprehensive aid so that they may better appreciate how emerging technology has been applied to cytology. Each chapter in this book presents a stand-alone contemporary review of emerging topics in cytopathology. We hope that you will find this monograph thought-provoking and a valuable reference for your practice. |
| digital image analysis pathology: Digital Pathology Liron Pantanowitz, Anil V. Parwani, 2017 The definitive, complete reference of digital pathology! An extraordinarily comprehensive and complete book for individuals with anything from minimal knowledge to deep, accomplished experience in digital pathology. Easy to read and plainly written, Digital Pathology examines the history and technological evolution of digital pathology, from the birth of scanning technology and telepathology to three-dimensional imaging on large multi-touch displays and computer aided diagnosis. A must-have book for anyone wishing to learn more about and work in this exciting and critical information environment including pathologists, laboratory professionals, students and any other medical practitioners with a particular interest in the history and future of digital pathology. It can also be a useful reference for anyone, medical or non-medical, who have an interest in learning more about the field. Digital pathology is truly a game changer, and this book is a crucial tool for anyone wishing to know more. Subjects discussed in depth include: Static digital imaging; basics and clinical use. Digital imaging processes. Telepathology. While slide imaging. Clinical applications of whole slide imaging. Digital pathology for educational, quality improvement, research and other settings. Forensic digital imaging. |
| digital image analysis pathology: Deep Learning for Medical Image Analysis S. Kevin Zhou, Hayit Greenspan, Dinggang Shen, 2023-11-23 Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache |
| digital image analysis pathology: Artificial Intelligence in Digital Pathology Image Analysis Min Tang, Jialiang Yang, Li Xiao, 2024-09-25 Thanks to the development and deployment of whole-slide imaging technology in pathology, glass slides previously observed under a traditional microscope are now scanned and converted to digital images, which are more beneficial for remote access, portability, and ease of sharing to facilitate telepathology. More importantly, digitization of glass slides paves the way towards the wide use of artificial intelligence (AI) tools including machine/deep learning algorithms, resulting in improved diagnostic accuracy. In the past decade, a large number of studies have demonstrated the remarkable success of AI, particularly deep learning, in digital pathology, such as tumor region identification, metastasis detection, and patient prognosis. Differing from handcrafted feature-based approaches that take advantage of domain knowledge to delineate specific morphological measurements (e.g., nuclei shape and size and tissue texture) in the images as features for training, deep learning is a paradigm of feature learning entirely driven by the image data and/or labels. Herein, the use of deep learning in pathological diagnosis can not only handle increased workloads and expertise shortages but also obviate subjective diagnosis from pathologists. Yet there remain many scientific and technological challenges associated with the efficiency of deep learning algorithms for use in clinical practice. For example, deep learning requires a sufficient amount of training data for generalization and suffers from a lack of feature interpretability. The overarching goal of this special issue is to highlight novel research accomplishments and directions, related to advanced AI methodology development and applications in digital pathology. |
| digital image analysis pathology: Image Processing and Analysis with Graphs Olivier Lezoray, Leo Grady, 2017-07-12 Covering the theoretical aspects of image processing and analysis through the use of graphs in the representation and analysis of objects, Image Processing and Analysis with Graphs: Theory and Practice also demonstrates how these concepts are indispensible for the design of cutting-edge solutions for real-world applications. Explores new applications in computational photography, image and video processing, computer graphics, recognition, medical and biomedical imaging With the explosive growth in image production, in everything from digital photographs to medical scans, there has been a drastic increase in the number of applications based on digital images. This book explores how graphs—which are suitable to represent any discrete data by modeling neighborhood relationships—have emerged as the perfect unified tool to represent, process, and analyze images. It also explains why graphs are ideal for defining graph-theoretical algorithms that enable the processing of functions, making it possible to draw on the rich literature of combinatorial optimization to produce highly efficient solutions. Some key subjects covered in the book include: Definition of graph-theoretical algorithms that enable denoising and image enhancement Energy minimization and modeling of pixel-labeling problems with graph cuts and Markov Random Fields Image processing with graphs: targeted segmentation, partial differential equations, mathematical morphology, and wavelets Analysis of the similarity between objects with graph matching Adaptation and use of graph-theoretical algorithms for specific imaging applications in computational photography, computer vision, and medical and biomedical imaging Use of graphs has become very influential in computer science and has led to many applications in denoising, enhancement, restoration, and object extraction. Accounting for the wide variety of problems being solved with graphs in image processing and computer vision, this book is a contributed volume of chapters written by renowned experts who address specific techniques or applications. This state-of-the-art overview provides application examples that illustrate practical application of theoretical algorithms. Useful as a support for graduate courses in image processing and computer vision, it is also perfect as a reference for practicing engineers working on development and implementation of image processing and analysis algorithms. |
| digital image analysis pathology: Digital Image Analysis S. Levialdi, 1984 |
| digital image analysis pathology: Digital Pathology Keith J. Kaplan, Luigi K.F. Rao, 2015-10-23 Digital Pathology: Historical Perspectives, Current Concepts & Future Applications provides the authoritative text in the digital pathology domain by combining the established expertise of leaders in this diverse arena with practical applications of this transformative platform while harnessing a content rich, interactive format. In detailing a cohesive narrative from a broad, global perspective the lessons learned from the past, the obstacles to digital pathology adoption that have been overcome and the challenges that remain for full realization of the potential that computational analysis affords, this text provides readers with the latest in where the field is heading as it seeks to unlock the potential of digital pathology by leveraging cutting edge technologies and innovative tools. Digital Pathology: Historical Perspectives, Current Concepts & Future Applications will be of great value to Pathologists including Translational Researchers and Informaticists, Pathology Trainees including Residents and Informatics Fellows, Healthcare CMIOs and CIOs, Bioimaging Engineers and Computer Vision Scientists, as well as Laboratory Information Technologists. |
| digital image analysis pathology: Picture Processing and Psychopictorics B.S. Lipkin, 1970-01-01 Picture Processing and Psychopictorics explores the selected aspects of perception and picture processing involving variables that are relevant to psychopictoric research. This book is organized into four parts encompassing 18 chapters. The first three parts cover the three classes of psychophysical variables, namely, contrast and border, shape and geometry, and texture. These parts also deal with the factors that influence the detection of objects in complex images. The discussion then shifts to the role of these factors in perception, as well as the computer analysis and manipulation of images with respect to these factors. The fourth part describes the programming systems for online experimental design and image manipulation. This work will be of great value to psychologists concerned with determining how the human extracts information from visual stimuli and to computer scientist concerned with developing programs and equipment to extract similar information from images. |
| digital image analysis pathology: Proceedings of COMPSTAT'2010 Yves Lechevallier, Gilbert Saporta, 2010-11-08 Proceedings of the 19th international symposium on computational statistics, held in Paris august 22-27, 2010.Together with 3 keynote talks, there were 14 invited sessions and more than 100 peer-reviewed contributed communications. |
| digital image analysis pathology: Deep Learning in Medical Image Analysis Zhengchao Dong, Juan Manuel Gorriz, 2021 The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis. |
| digital image analysis pathology: Principles And Advanced Methods In Medical Imaging And Image Analysis Atam P Dhawan, Bernie H K Huang, Dae-shik Kim, 2008-03-17 Computerized medical imaging and image analysis have been the central focus in diagnostic radiology. They provide revolutionalizing tools for the visualization of physiology as well as the understanding and quantitative measurement of physiological parameters. This book offers in-depth knowledge of medical imaging instrumentation and techniques as well as multidimensional image analysis and classification methods for research, education, and applications in computer-aided diagnostic radiology. Internationally renowned researchers and experts in their respective areas provide detailed descriptions of the basic foundation as well as the most recent developments in medical imaging, thus helping readers to understand theoretical and advanced concepts for important research and clinical applications. |
| digital image analysis pathology: Perspectives on Digital Pathology Marcial García-Rojo, Bernd Blobel, Arvydas Laurinavicius, 2012 Multimedia information and digital images are increasingly important in the field of healthcare, but establishing an adequate technological framework for their management, and workable international standards to ensure compatibility and interoperability, are crucial if they are to be employed effectively. This book presents the main research efforts of EURO-TELEPATH, an initiative of the European Corporation in Science and Technology (COST) Action, IC0604. This program began in November 2007, and ran until November 2011. Its aim was to develop the standards and solutions necessary to represent, interpret, browse and retrieve digital medical images, while preserving their diagnostic quality for clinical purposes, education and research. At the end of the project, the most relevant researchers in the field of digital pathology u many of whom had been active members of EURO-TELEPATH u were asked to contribute to a book which would compile the main research efforts of the European COST Action consortium. The book is divided into six parts. The first is an introduction to the instruments and activities of COST.This is followed by sections dealing with: the state-of-the-art in pathology; pathology business modeling; standards and specifications in pathology; the analysis, processing, retrieval and management of images; technology and automation in pathology; and strategic developments and emerging research. As well as being a comprehensive overview of the IC0604 COST program, the book includes a selection of papers from American and Japanese researchers working in the same field. |
| digital image analysis pathology: Digital Image Processing and Analysis Scott E Umbaugh, 2017-11-30 Digital image processing and analysis is a field that continues to experience rapid growth, with applications in many facets of our lives. Areas such as medicine, agriculture, manufacturing, transportation, communication systems, and space exploration are just a few of the application areas. This book takes an engineering approach to image processing and analysis, including more examples and images throughout the text than the previous edition. It provides more material for illustrating the concepts, along with new PowerPoint slides. The application development has been expanded and updated, and the related chapter provides step-by-step tutorial examples for this type of development. The new edition also includes supplementary exercises, as well as MATLAB-based exercises, to aid both the reader and student in development of their skills. |
| digital image analysis pathology: Computer Imaging Scott E Umbaugh, 2005-01-27 Computer Imaging: Digital Image Analysis and Processing brings together analysis and processing in a unified framework, providing a valuable foundation for understanding both computer vision and image processing applications. Taking an engineering approach, the text integrates theory with a conceptual and application-oriented style, allowing you to immediately understand how each topic fits into the overall structure of practical application development. Divided into five major parts, the book begins by introducing the concepts and definitions necessary to understand computer imaging. The second part describes image analysis and provides the tools, concepts, and models required to analyze digital images and develop computer vision applications. Part III discusses application areas for the processing of images, emphasizing human visual perception. Part IV delivers the information required to apply a CVIPtools environment to algorithm development. The text concludes with appendices that provide supplemental imaging information and assist with the programming exercises found in each chapter. The author presents topics as needed for understanding each practical imaging model being studied. This motivates the reader to master the topics and also makes the book useful as a reference. The CVIPtools software integrated throughout the book, now in a new Windows version, provides practical examples and encourages you to conduct additional exploration via tutorials and programming exercises provided with each chapter. |
| digital image analysis pathology: Biomedical Data Mining for Information Retrieval Sujata Dash, Subhendu Kumar Pani, S. Balamurugan, Ajith Abraham, 2021-08-24 BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics. |
| digital image analysis pathology: Image Analysis and Recognition Aurélio Campilho, Fakhri Karray, Bart ter Haar Romeny, 2018-06-06 This book constitutes the thoroughly refereed proceedings of the 15th International Conference on Image Analysis and Recognition, ICIAR 2018, held in Póvoa de Varzim, Portugal, in June 2018. The 91 full papers presented together with 15 short papers were carefully reviewed and selected from 179 submissions. The papers are organized in the following topical sections: Enhancement, Restoration and Reconstruction, Image Segmentation, Detection, Classication and Recognition, Indexing and Retrieval, Computer Vision, Activity Recognition, Traffic and Surveillance, Applications, Biomedical Image Analysis, Diagnosis and Screening of Ophthalmic Diseases, and Challenge on Breast Cancer Histology Images. |
| digital image analysis pathology: Medical Image Analysis and Informatics Paulo Mazzoncini de Azevedo-Marques, Arianna Mencattini, Marcello Salmeri, Rangaraj M. Rangayyan, 2017-11-23 With the development of rapidly increasing medical imaging modalities and their applications, the need for computers and computing in image generation, processing, visualization, archival, transmission, modeling, and analysis has grown substantially. Computers are being integrated into almost every medical imaging system. Medical Image Analysis and Informatics demonstrates how quantitative analysis becomes possible by the application of computational procedures to medical images. Furthermore, it shows how quantitative and objective analysis facilitated by medical image informatics, CBIR, and CAD could lead to improved diagnosis by physicians. Whereas CAD has become a part of the clinical workflow in the detection of breast cancer with mammograms, it is not yet established in other applications. CBIR is an alternative and complementary approach for image retrieval based on measures derived from images, which could also facilitate CAD. This book shows how digital image processing techniques can assist in quantitative analysis of medical images, how pattern recognition and classification techniques can facilitate CAD, and how CAD systems can assist in achieving efficient diagnosis, in designing optimal treatment protocols, in analyzing the effects of or response to treatment, and in clinical management of various conditions. The book affirms that medical imaging, medical image analysis, medical image informatics, CBIR, and CAD are proven as well as essential techniques for health care. |
| digital image analysis pathology: Pathology of the Breast Fattaneh A. Tavassoli, 1999-01-01 The most complete reference on breast disease today, this book provides comprehensive information on all benign and malignant lesions of the breast. Here is the necessary focus on the wide variety of facts and detail pathologists need to make precise, critical diagnoses. |
| digital image analysis pathology: Biomedical Image Processing Thomas Martin Deserno, 2011-03-01 In modern medicine, imaging is the most effective tool for diagnostics, treatment planning and therapy. Almost all modalities have went to directly digital acquisition techniques and processing of this image data have become an important option for health care in future. This book is written by a team of internationally recognized experts from all over the world. It provides a brief but complete overview on medical image processing and analysis highlighting recent advances that have been made in academics. Color figures are used extensively to illustrate the methods and help the reader to understand the complex topics. |
| digital image analysis pathology: Mammographic Image Analysis Ralph Highnam, Michael Brady, 1999 The key contribution of the approach to x-ray mammographic image analysis developed in this monograph is a representation of the non-fatty compressed breast tissue that we show can be derived from a single mammogram. The importance of the representation, called hint, is that it removes all those changes in the image that are due only to the particular imaging conditions (for example, the film speed or exposure time), leaving just the non-fatty 'interesting' tissue. Normalising images in this way enables them to be enhanced and matched, and regions in them to be classified more reliably, because unnecessary, distracting variations have been eliminated. Part I of the monograph develops a model-based approach to x-ray mammography, Part II shows how it can be put to work successfully on a range of clinically-important tasks, while Part III develops a model and exploits it for contrast-enhanced MRI mammography. The final chapter points the way forward in a number of promising areas of research. |
| digital image analysis pathology: Handbook of Biological Confocal Microscopy James Pawley, 2013-04-17 This third edition of a classic text in biological microscopy includes detailed descriptions and in-depth comparisons of parts of the microscope itself, digital aspects of data acquisition and properties of fluorescent dyes, the techniques of 3D specimen preparation and the fundamental limitations, and practical complexities of quantitative confocal fluorescence imaging. Coverage includes practical multiphoton, photodamage and phototoxicity, 3D FRET, 3D microscopy correlated with micro-MNR, CARS, second and third harmonic signals, ion imaging in 3D, scanning RAMAN, plant specimens, practical 3D microscopy and correlated optical tomography. |
| digital image analysis pathology: Soft Computing Based Medical Image Analysis Nilanjan Dey, Amira S. Ashour, Fuquian Shi, Valentina Emilia Balas, 2018-01-18 Soft Computing Based Medical Image Analysis presents the foremost techniques of soft computing in medical image analysis and processing. It includes image enhancement, segmentation, classification-based soft computing, and their application in diagnostic imaging, as well as an extensive background for the development of intelligent systems based on soft computing used in medical image analysis and processing. The book introduces the theory and concepts of digital image analysis and processing based on soft computing with real-world medical imaging applications. Comparative studies for soft computing based medical imaging techniques and traditional approaches in medicine are addressed, providing flexible and sophisticated application-oriented solutions. - Covers numerous soft computing approaches, including fuzzy logic, neural networks, evolutionary computing, rough sets and Swarm intelligence - Presents transverse research in soft computing formation from various engineering and industrial sectors in the medical domain - Highlights challenges and the future scope for soft computing based medical analysis and processing techniques |
| digital image analysis pathology: Image Processing: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2013-05-31 Advancements in digital technology continue to expand the image science field through the tools and techniques utilized to process two-dimensional images and videos. Image Processing: Concepts, Methodologies, Tools, and Applications presents a collection of research on this multidisciplinary field and the operation of multi-dimensional signals with systems that range from simple digital circuits to computers. This reference source is essential for researchers, academics, and students in the computer science, computer vision, and electrical engineering fields. |
| digital image analysis pathology: Pathology Informatics: Theory and Practice Liron Pantanowitz, 2012 Pathology Informatics: Theory & Practice is the first multi- authored, current and comprehensive compendium of the diverse and rapidly expanding field of pathology informatics. It includes all of the critical and practical advice for management, operations, budgeting, and project planning and will serve as a comprehensive review of the field for students, pathologists, and laboratory professionals. This book deals with the role of computing hardware, software and databases involved in the efficient information management for pathology practice, as well as the fundamental science of informatics that is so deeply embedded in this subspecialty. The text builds from basic principles of computer theory to more sophisticated informatics concepts. Databases and data mining; networks and workstations; system interfaces and interoperability. Bioinformatics, imaging informatics, clinical informatics, and public health informatics. Automation and middleware that facilitate complex workflows encountered in both anatomic and clinical pathology practice. Molecular testing and point of care solutions. Coding and nomenclature. Standards in Laboratory Information Systems (LIS) and imaging systems. Project management and business skills. Pathology reporting. Electronic medical records. Specimen tracking and identification. Error reduction and quality management. Training and education in pathology informatics. |
| digital image analysis pathology: Advanced Imaging Techniques in Clinical Pathology Francesco M. Sacerdoti, Antonio Giordano, Carlo Cavaliere, 2018-04-21 This text provides a comprehensive, state-of-the-art review of the application of image analysis focusing on the techniques which can be used in every biology and medical laboratory to automate procedures of cell analysis and to create statistics very useful for a comprehension of cell growth dynamics and the effects of drugs on them. This textbook will serve as a very useful resource for physicians and researchers dealing with, and interested in, cell analysis. It will provide a concise yet comprehensive summary of the current status of the field that will help guide patient management and stimulate investigative efforts. All chapters are written by experts in their fields and include the most up-to-date scientific and clinical information. Advanced Imaging Techniques in Clinical Pathology will be of great value to clinical pathologists, biologists, biology researchers, and those working in the clinical and biological laboratory arena. |
| digital image analysis pathology: Medical Imaging Informatics Alex A.T. Bui, Ricky K. Taira, 2009-12-01 Medical Imaging Informatics provides an overview of this growing discipline, which stems from an intersection of biomedical informatics, medical imaging, computer science and medicine. Supporting two complementary views, this volume explores the fundamental technologies and algorithms that comprise this field, as well as the application of medical imaging informatics to subsequently improve healthcare research. Clearly written in a four part structure, this introduction follows natural healthcare processes, illustrating the roles of data collection and standardization, context extraction and modeling, and medical decision making tools and applications. Medical Imaging Informatics identifies core concepts within the field, explores research challenges that drive development, and includes current state-of-the-art methods and strategies. |
| digital image analysis pathology: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan, 2019-10-10 The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging. |
| digital image analysis pathology: Biomedical Signal and Image Processing with Artificial Intelligence Chirag Paunwala, Mita Paunwala, Rahul Kher, Falgun Thakkar, Heena Kher, Mohammed Atiquzzaman, Norliza Mohd. Noor, 2023-01-09 This book focuses on advanced techniques used for feature extraction, analysis, recognition, and classification in the area of biomedical signal and image processing. Contributions cover all aspects of artificial intelligence, machine learning, and deep learning in the field of biomedical signal and image processing using novel and unexplored techniques and methodologies. The book covers recent developments in both medical images and signals analyzed by artificial intelligence techniques. The authors also cover topics related to development based artificial intelligence, which includes machine learning, neural networks, and deep learning. This book will provide a platform for researchers who are working in the area of artificial intelligence for biomedical applications. Provides insights into medical signal and image analysis using artificial intelligence; Includes novel and recent trends of decision support system for medical research; Outlines employment of evolutionary algorithms for biomedical data, big data analysis for medical databases, and reliability, opportunities, and challenges in clinical data. |
| digital image analysis pathology: Comparative Anatomy and Histology Piper M. Treuting, Suzanne M. Dintzis, Kathleen S. Montine, 2017-08-29 The second edition of Comparative Anatomy and Histology is aimed at the new rodent investigator as well as medical and veterinary pathologists who need to expand their knowledge base into comparative anatomy and histology. It guides the reader through normal mouse and rat anatomy and histology using direct comparison to the human. The side by side comparison of mouse, rat, and human tissues highlight the unique biology of the rodents, which has great impact on the validation of rodent models of human disease. - Offers the only comprehensive source for comparing mouse, rat, and human anatomy and histology through over 1500 full-color images, in one reference work - Enables human and veterinary pathologists to examine tissue samples with greater accuracy and confidence - Teaches biomedical researchers to examine the histologic changes in their model rodents - Experts from both human and veterinary fields take readers through each organ system in a side-by-side comparative approach to anatomy and histology - human Netter anatomy images along with Netter-style rodent images |
| digital image analysis pathology: Medical Image Understanding and Analysis María Valdés Hernández, Víctor González-Castro, 2017-06-20 This book constitutes the refereed proceedings of the 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, held in Edinburgh, UK, in July 2017. The 82 revised full papers presented were carefully reviewed and selected from 105 submissions. The papers are organized in topical sections on retinal imaging, ultrasound imaging, cardiovascular imaging, oncology imaging, mammography image analysis, image enhancement and alignment, modeling and segmentation of preclinical, body and histological imaging, feature detection and classification. The chapters 'Model-Based Correction of Segmentation Errors in Digitised Histological Images' and 'Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering' are open access under a CC BY 4.0 license. |
| digital image analysis pathology: Systems Medicine , 2020-08-24 Technological advances in generated molecular and cell biological data are transforming biomedical research. Sequencing, multi-omics and imaging technologies are likely to have deep impact on the future of medical practice. In parallel to technological developments, methodologies to gather, integrate, visualize and analyze heterogeneous and large-scale data sets are needed to develop new approaches for diagnosis, prognosis and therapy. Systems Medicine: Integrative, Qualitative and Computational Approaches is an innovative, interdisciplinary and integrative approach that extends the concept of systems biology and the unprecedented insights that computational methods and mathematical modeling offer of the interactions and network behavior of complex biological systems, to novel clinically relevant applications for the design of more successful prognostic, diagnostic and therapeutic approaches. This 3 volume work features 132 entries from renowned experts in the fields and covers the tools, methods, algorithms and data analysis workflows used for integrating and analyzing multi-dimensional data routinely generated in clinical settings with the aim of providing medical practitioners with robust clinical decision support systems. Importantly the work delves into the applications of systems medicine in areas such as tumor systems biology, metabolic and cardiovascular diseases as well as immunology and infectious diseases amongst others. This is a fundamental resource for biomedical students and researchers as well as medical practitioners who need to need to adopt advances in computational tools and methods into the clinical practice. Encyclopedic coverage: ‘one-stop’ resource for access to information written by world-leading scholars in the field of Systems Biology and Systems Medicine, with easy cross-referencing of related articles to promote understanding and further research Authoritative: the whole work is authored and edited by recognized experts in the field, with a range of different expertise, ensuring a high quality standard Digitally innovative: Hyperlinked references and further readings, cross-references and diagrams/images will allow readers to easily navigate a wealth of information |
| digital image analysis pathology: Medical Image Processing Geoff Dougherty, 2011-07-25 The book is designed for end users in the field of digital imaging, who wish to update their skills and understanding with the latest techniques in image analysis. The book emphasizes the conceptual framework of image analysis and the effective use of image processing tools. It uses applications in a variety of fields to demonstrate and consolidate both specific and general concepts, and to build intuition, insight and understanding. Although the chapters are essentially self-contained they reference other chapters to form an integrated whole. Each chapter employs a pedagogical approach to ensure conceptual learning before introducing specific techniques and “tricks of the trade”. The book concentrates on a number of current research applications, and will present a detailed approach to each while emphasizing the applicability of techniques to other problems. The field of topics is wide, ranging from compressive (non-uniform) sampling in MRI, through automated retinal vessel analysis to 3-D ultrasound imaging and more. The book is amply illustrated with figures and applicable medical images. The reader will learn the techniques which experts in the field are currently employing and testing to solve particular research problems, and how they may be applied to other problems. |
| digital image analysis pathology: Signal. Image. Architecture John May, 2019 Architecture is immersed in an immense cultural experiment called imaging. Yet the technical status and nature of that imaging must be reevaluated. What happens to the architectural mind when it stops pretending that electronic images of drawings made by computers are drawings? When it finally admits that imaging is not drawing, but is instead something that has already obliterated drawing? These are questions that, in general, architecture has scarcely begun to pose, imagining that somehow its ideas and practices can resist the culture of imaging in which the rest of life now either swims or drowns. To patiently describe the world to oneself is to prepare the ground for an as yet unavailable politics. New descriptions can, under the right circumstances, be made to serve as the raw substrate for political impulses that cannot yet be expressed or lived, because their preconditions have not been arranged and articulated. Signal. Image. Architecture. aims to clarify the status of computational images in contemporary architectural thought and practice by showing what happens if the technical basis of architecture is examined very closely, if its technical terms and concepts are taken very seriously, at times even literally. It is not a theory of architectural images, but rather a brief philosophical description of architecture after imaging. |
Digital pathology and computational image analysis in
Aug 26, 2020 · In this Review, we discuss how developments in digital pathology and computational image analysis are shaping a new digital era for nephropathology and how …
Validation of Digital Pathology In a Healthcare Environment
Digital pathology, as defined by the Digital Pathology Association, is a dynamic, image-based environment that enables the acquisition, management and interpretation of pathology …
Pathology Image Analysis Using Segmentation Deep Learning …
In this review, the pathology image segmentation process using deep learning algorithms is described in detail. The goals are to provide quick guidance for implementing deep learning …
Deep Learning Models for Digital Pathology - arXiv.org
In this report, we identi ed and reviewed 85 published works that form the state-of-the-art in terms of image analysis and deep learning methods tailored primarily for digital pathology images.
Digital pathology image analysis with deep learning - univ …
Digital Pathology (DP) includes the process of digitizing histopathology slides using whole-slide scanners as well as the analysis of these digitized whole-slide images (WSI) using …
Deep learning in digital pathology image analysis: a survey
In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classi cation, semantic segmentation, detection, …
Deep Learning for Digital Image Analysis with Whole Slide …
The advent of whole slide imaging in digital pathology has introduced the computer-aided diagnosis of tissue via digital image analysis. Digitized slides can now be analyzed via a …
Developing Image Analysis Methods for Digital Pathology
The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite novel image analysis methods for pathology …
Deep learning applications in digital pathology - Nature
Deep Learning (DL) holds great promise to improve patient outcomes by improving the precision and speed of disease diagnosis and treatment recommendations. Given the eficacy of DL in …
Digital pathology image analysis: opportunities and challenges
The digital pathologist & computerized image analysis of histopathology Over the last decade, the nature of diagnostic healthcare has changed rapidly owing to an explosion in the availability of …
Visual Analytics in Digital Pathology: Challenges and …
In this paper, we characterize digital pathology from a VA perspective, by identifying the challenges and the opportunities for employing VA techniques in the daily workflow of the …
Digital Pathology for Routine Histopathology Diagnosis
Complete digital pathology and whole slide imaging (WSI) for routine histopathology diagnosis is currently in use in some laboratories worldwide ( V. N. Newitt, CAP Today Sept 2019 )
High-performance Data Management for Whole Slide Image …
Our focus has been constructing and releasing a digital pathology-centric pipeline using ADIOS2, which facilitates streamlined data management across WSIs. Additionally, we’ve developed …
Artificial intelligence in digital pathology: a systematic review …
Therefore, the present study is the first systematic review and meta-analysis to address the diagnostic accuracy of AI across all disease areas in digital pathology, and includes studies...
The use of digital pathology and image analysis in clinical trials
Mar 25, 2019 · Digital pathology and image analysis potentially provide greater accuracy, reproducibility and standardisation of pathology-based trial entry criteria and endpoints, …
Best practice recommendations for implementing digital …
These Best Practice Recommendations (BPRs) provide an overview of the technology involved in digital pathology and of the currently available evidence on its diagnostic use, together with …
Digital image analysis in breast pathology from image …
In this review, we aim to summarize the recent de-velopments in digital image analysis and in the use of AI in forms of machine learning in diagnostic breast pa-thology and to investigate why …
Digital pathology and artificial intelligence in translational
The use of digital image analysis in pathology can identify and quantify specific cell types quickly and accurately and can quantitatively evaluate histological features, morphological pat-
Special Issue on Digital Pathology, Tissue Image Analysis, …
Oct 12, 2020 · Digital tissue image analysis has enabled users to extract quantitative and complex data from digitized whole-slide images. The following editorial provides an overview of the …
QuPath: Open source software for digital pathology image …
QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis.
Digital pathology and computational image analysis in
Aug 26, 2020 · In this Review, we discuss how developments in digital pathology and computational image analysis are shaping a new digital era for nephropathology and how …
Validation of Digital Pathology In a Healthcare Environment
Digital pathology, as defined by the Digital Pathology Association, is a dynamic, image-based environment that enables the acquisition, management and interpretation of pathology …
Pathology Image Analysis Using Segmentation Deep …
In this review, the pathology image segmentation process using deep learning algorithms is described in detail. The goals are to provide quick guidance for implementing deep learning …
Deep Learning Models for Digital Pathology - arXiv.org
In this report, we identi ed and reviewed 85 published works that form the state-of-the-art in terms of image analysis and deep learning methods tailored primarily for digital pathology images.
Digital pathology image analysis with deep learning - univ …
Digital Pathology (DP) includes the process of digitizing histopathology slides using whole-slide scanners as well as the analysis of these digitized whole-slide images (WSI) using …
Deep learning in digital pathology image analysis: a survey
In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classi cation, semantic segmentation, detection, …
Deep Learning for Digital Image Analysis with Whole Slide …
The advent of whole slide imaging in digital pathology has introduced the computer-aided diagnosis of tissue via digital image analysis. Digitized slides can now be analyzed via a …
Developing Image Analysis Methods for Digital Pathology
The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite novel image analysis methods for pathology …
Deep learning applications in digital pathology - Nature
Deep Learning (DL) holds great promise to improve patient outcomes by improving the precision and speed of disease diagnosis and treatment recommendations. Given the eficacy of DL in …
Digital pathology image analysis: opportunities and …
The digital pathologist & computerized image analysis of histopathology Over the last decade, the nature of diagnostic healthcare has changed rapidly owing to an explosion in the availability of …
Visual Analytics in Digital Pathology: Challenges and …
In this paper, we characterize digital pathology from a VA perspective, by identifying the challenges and the opportunities for employing VA techniques in the daily workflow of the …
Digital Pathology for Routine Histopathology Diagnosis
Complete digital pathology and whole slide imaging (WSI) for routine histopathology diagnosis is currently in use in some laboratories worldwide ( V. N. Newitt, CAP Today Sept 2019 )
High-performance Data Management for Whole Slide Image …
Our focus has been constructing and releasing a digital pathology-centric pipeline using ADIOS2, which facilitates streamlined data management across WSIs. Additionally, we’ve developed …
Artificial intelligence in digital pathology: a systematic review …
Therefore, the present study is the first systematic review and meta-analysis to address the diagnostic accuracy of AI across all disease areas in digital pathology, and includes studies...
The use of digital pathology and image analysis in clinical …
Mar 25, 2019 · Digital pathology and image analysis potentially provide greater accuracy, reproducibility and standardisation of pathology-based trial entry criteria and endpoints, …
Best practice recommendations for implementing digital …
These Best Practice Recommendations (BPRs) provide an overview of the technology involved in digital pathology and of the currently available evidence on its diagnostic use, together with …
Digital image analysis in breast pathology from image …
In this review, we aim to summarize the recent de-velopments in digital image analysis and in the use of AI in forms of machine learning in diagnostic breast pa-thology and to investigate why …
Digital pathology and artificial intelligence in translational
The use of digital image analysis in pathology can identify and quantify specific cell types quickly and accurately and can quantitatively evaluate histological features, morphological pat-
Special Issue on Digital Pathology, Tissue Image Analysis, …
Oct 12, 2020 · Digital tissue image analysis has enabled users to extract quantitative and complex data from digitized whole-slide images. The following editorial provides an overview of the …
QuPath: Open source software for digital pathology image …
QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis.
