Book series


Medical Image Recognition, Segmentation and Parsing Machine Learning and Multiple Object Approaches
Kevin Zhou, Principal Key Expert and Research Group Head, Whole Body Image Analytics

Learn and apply methods and algorithms for automatically recognizing, segmenting and parsing multiple objects

ISBN: 978-0-12-802581-9
PUB DATE: December 2015
AUDIENCE: Industry practitioners and university researchers in medical imaging.



  • A comprehensive overview of state-of-the-art research on medical image recognition, segmentation and parsing of multiple objects
  • Efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets
  • Algorithms for recognizing and parsing of multiple known anatomies for practical applications


This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image.

Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects.


  • Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects
  • Methods and theories for medical image recognition, segmentation and parsing of multiple objects
  • Efficient and effective machine learning solutions based on big datasets
  • Selected applications of medical image parsing using proven algorithms

Machine Learning and Medical Imaging
Edited by: Guorong Wu University of North Carolina at Chapel Hill, USA Dinggang Shen Professor, Department of Radiology and BRIC, UNC-Chapel Hill, USA Mert Sabuncu Assistant Professor of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA


Learn how to apply machine learning methods to medical imaging

ISBN: 978-0-12-804076-8
PUB DATE: August 2016
AUDIENCE Computer scientists, electronic and biomedical engineers researching in medical imaging, undergraduate and graduate students




  • Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems
  • Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics
  • Features self-contained chapters with a thorough literature review
  • Assesses the development of future machine learning techniques and the further application of existing techniques


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.


Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting
Edited by: Balocco Simone Associate professor, University of Barcelona, Spain, Maria A. Zuluaga Research Associate, University College London, UK, Guillaume Zahnd Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands , Su-Lin Lee Lecturer, Hamlyn Centre for Robotic Surgery, Ipmerial College London, UK AND Stefanie Demirci Postdoctoral Researcher and Research Manager at Technical University of Munich (TUM), Germany


Applies deep learning methods to medical imaging, providing a clear understanding of the principles and methods of neural network and deep learning

ISBN: 978-0-12-811018-8
PUB DATE: December 2016
AUDIENCE: Biomedical engineers, medical imaging researchers, cardiologists, clinicians



  • Brings together scientific researchers, medical experts and industry partners working in the field of endovascular imaging and stenting procedures within different anatomical regions
  • An introduction to the clinical workflow and current challenges in endovascular Interventions
  • A review of the state-of-the-art methodologies in endovascular imaging and their applications
  • Presents outstanding questions still to be solved and discusses future research


This book presents imaging, treatment and computed assisted technological techniques for diagnostic and intraoperative vascular imaging and stenting. These techniques offer increasingly useful information on vascular anatomy and function, and are poised to have a dramatic impact on the diagnosis, analysis, modeling, and treatment of vascular diseases.

After setting out the technical and clinical challenges of vascular imaging and stenting, this book gives a concise overview of the basics before presenting state-of-the-art methods for solving these challenges. 

The reader will learn:

  • The main challenges in endovascular procedures
  • New applications of intravascular imaging
  • The latest advances in computer assisted stenting

Deep Learning for Medical Image Analysis
Edited by: Kevin Zhou Principal Key Expert, Medical Image Analysis, Siemens Healthcare Technology Center, Princeton, New Jersey, USA Hayit Greenspan Head, Medical Image Processing and Analysis Lab, Biomedical Engineering Department, Faculty of Engineering, Tel-Aviv University, Israel Dinggang Shen Professor, Department of Radiology and BRIC, UNC-Chapel Hill, USA

Applies deep learning methods to medical imaging, providing a clear understanding of the principles and methods of neural network and deep learning

ISBN: 978-0-12-810408-8
PUB DATE: January 2017
AUDIENCE:  Academic and industry researchers and graduate students in medical imaging, computer vision, biomedical engineering



  • Covers common research problems in medical image analysis and their challenges
  • Describes 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 Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc.


Deep learning is providing exciting solutions for medical image analysis problems and is seen as 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 have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas.

Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.