Books in the The MICCAI Society book Series series

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) SocietyContains state-of-the-art technical approaches to key challengesDemonstrates proven algorithms for a whole range of essential medical imaging applicationsIncludes source codes for use in a plug-and-play mannerEmbraces future directions in the fields of medical image computing and computer-assisted intervention

Connectomics: Applications to Neuroimaging is unique in presenting the frontier of neuro-applications using brain connectomics techniques. The book describes state-of-the-art research that applies brain connectivity analysis techniques to a broad range of neurological and psychiatric disorders (Alzheimer''s, epilepsy, stroke, autism, Parkinson''s, drug or alcohol addiction, depression, bipolar, and schizophrenia), brain fingerprint applications, speech-language assessments, and cognitive assessment. With this book the reader will learn: Basic mathematical principles underlying connectomicsHow connectomics is applied to a wide range of neuro-applicationsWhat is the future direction of connectomics techniques. This book is an ideal reference for researchers and graduate students in computer science, data science, computational neuroscience, computational physics, or mathematics who need to understand how computational models derived from brain connectivity data are being used in clinical applications, as well as neuroscientists and medical researchers wanting an overview of the technical methods. Features: Combines connectomics methods with relevant and interesting neuro-applicationsCovers most of the hot topics in neuroscience and clinical areasAppeals to researchers in a wide range of disciplines: computer science, engineering, data science, mathematics, computational physics, computational neuroscience, as well as neuroscience, and medical researchers interested in the technical methods of connectomicsCombines connectomics methods with relevant and interesting neuro-applicationsPresents information that will appeal to researchers in a wide range of disciplines, including computer science, engineering, data science, mathematics, computational physics, computational neuroscience, and more Includes a mathematics primer that formulates connectomics from an applied point-of-view, thus avoiding difficult to understand theoretical perspectiveLists publicly available neuro-imaging datasets that can be used to construct structural and functional connectomes

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.Demonstrates the application of cutting-edge machine learning techniques to medical imaging problemsCovers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomicsFeatures self-contained chapters with a thorough literature reviewAssesses the development of future machine learning techniques and the further application of existing techniques