Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges


Book Description

This book constitutes the thoroughly refereed post-workshop proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 52 revised full workshop papers were carefully reviewed and selected from 60 submissions. The topics of the workshop included: cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods.




Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges


Book Description

This book constitutes the thoroughly refereed post-workshop proceedings of the 10th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 42 revised full workshop papers were carefully reviewed and selected from 76 submissions. The topics of the workshop included: cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods.







Current and Future Role of Artificial Intelligence in Cardiac Imaging


Book Description

This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.




Left Atrial and Scar Quantification and Segmentation


Book Description

This book constitutes the First Left Atrial and Scar Quantification and Segmentation Challenge, LAScarQS 2022, which was held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, in Singapore, in September 2022. The 15 papers presented in this volume were carefully reviewed and selected form numerous submissions. The aim of the challenge is not only benchmarking various LA scar segmentation algorithms, but also covering the topic of general cardiac image segmentation, quantification, joint optimization, and model generalization, and raising discussions for further technical development and clinical deployment.




Handbook of Medical Image Computing and Computer Assisted Intervention


Book Description

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. - Presents the key research challenges in medical image computing and computer-assisted intervention - Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society - Contains state-of-the-art technical approaches to key challenges - Demonstrates proven algorithms for a whole range of essential medical imaging applications - Includes source codes for use in a plug-and-play manner - Embraces future directions in the fields of medical image computing and computer-assisted intervention




Computer-Aided Oral and Maxillofacial Surgery


Book Description

Computer-Aided Oral and Maxillofacial Surgery: Developments, Applications, and Future Perspectives is an ideal resource for biomedical engineers and computer scientists, clinicians and clinical researchers looking for an understanding on the latest technologies applied to oral and maxillofacial surgery. In facial surgery, computer-aided decisions supplement all kind of treatment stages, from a diagnosis to follow-up examinations. This book gives an in-depth overview of state-of-the-art technologies, such as deep learning, augmented reality, virtual reality and intraoperative navigation, as applied to oral and maxillofacial surgery. It covers applications of facial surgery that are at the interface between medicine and computer science. Examples include the automatic segmentation and registration of anatomical and pathological structures, like tumors in the facial area, intraoperative navigation in facial surgery and its recent developments and challenges for treatments like zygomatic implant placement. - Provides comprehensive, state-of-the-art knowledge of interdisciplinary applications in facial surgery - Presents recent algorithmic developments like Deep Learning, along with recent devices in augmented reality and virtual reality - Includes clinical knowledge of two facials surgeons who give insights into the current clinical practice and challenges of facial surgeons in university hospitals in Austria and China




Uncertainty for Safe Utilization of Machine Learning in Medical Imaging


Book Description

This book constitutes the refereed proceedings of the 5th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2023, held in conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. For this workshop, 21 papers from 32 submissions were accepted for publication. The accepted papers cover the fields of uncertainty estimation and modeling, as well as out of distribution management, domain shift robustness, Bayesian deep learning and uncertainty calibration.




Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning


Book Description

During these uncertain and turbulent times, intelligent technologies including artificial neural networks (ANN) and machine learning (ML) have played an incredible role in being able to predict, analyze, and navigate unprecedented circumstances across a number of industries, ranging from healthcare to hospitality. Multi-factor prediction in particular has been especially helpful in dealing with the most current pressing issues such as COVID-19 prediction, pneumonia detection, cardiovascular diagnosis and disease management, automobile accident prediction, and vacation rental listing analysis. To date, there has not been much research content readily available in these areas, especially content written extensively from a user perspective. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning is designed to cover a brief and focused range of essential topics in the field with perspectives, models, and first-hand experiences shared by prominent researchers, discussing applications of artificial neural networks (ANN) and machine learning (ML) for biomedical and business applications and a listing of current open-source software for neural networks, machine learning, and artificial intelligence. It also presents summaries of currently available open source software that utilize neural networks and machine learning. The book is ideal for professionals, researchers, students, and practitioners who want to more fully understand in a brief and concise format the realm and technologies of artificial neural networks (ANN) and machine learning (ML) and how they have been used for prediction of multi-disciplinary research problems in a multitude of disciplines.




Medical Imaging and Computer-Aided Diagnosis


Book Description

This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging. Given its coverage, the book provides both a forum and valuable resource for researchers involved in image formation, experimental methods, image performance, segmentation, pattern recognition, feature extraction, classifier design, machine learning / deep learning, radiomics, CAD workstation design, human–computer interaction, databases, and performance evaluation.