Dr. Ziv R. Yaniv
at National Institute of Allergy and Infectious Diseases
SPIE Involvement:
Conference Program Committee | Conference Chair | Author | Editor | Instructor
Websites:
Publications (22)

Proceedings Article | 12 March 2018
Proc. SPIE. 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling
KEYWORDS: Image processing algorithms and systems, Magnetic resonance imaging, Image segmentation, 3D modeling, Image registration, Medical imaging, Cardiovascular magnetic resonance imaging

Proceedings Article | 24 March 2016
Proc. SPIE. 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
KEYWORDS: Visualization, Cameras, Magnetic resonance imaging, Image registration, Data acquisition, Light sources and illumination, Skull, Medical devices, Rigid registration, Algorithm development, New and emerging technologies

Proceedings Article | 18 March 2015
Proc. SPIE. 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling
KEYWORDS: Optical spheres, Data modeling, Calibration, Robotics, Adaptive optics, Haptic technology, Image registration, Data acquisition, Head, Optical tracking

Showing 5 of 22 publications
Conference Committee Involvement (13)
Image-Guided Procedures, Robotic Interventions, and Modeling
16 February 2020 | Houston, Texas, United States
Image-Guided Procedures, Robotic Interventions, and Modeling
17 February 2019 | San Diego, California, United States
Image-Guided Procedures, Robotic Interventions, and Modeling
12 February 2018 | Houston, Texas, United States
Image-Guided Procedures, Robotic Interventions, and Modeling Posters
14 February 2017 | Orlando, FL, United States
Image-Guided Procedures, Robotic Interventions, and Modeling
14 February 2017 | Orlando, Florida, United States
Showing 5 of 13 Conference Committees
Course Instructor
SC1236: SimpleITK Jupyter Notebooks: Biomedical Image Analysis in Python
SimpleITK is a simplified programming interface to the algorithms and data structures of the Insight Segmentation and Registration Toolkit (ITK). It supports bindings for multiple programming languages including C++, Python, R, Java, C#, Lua, Ruby and TCL. Combining SimpleITK’s Python binding with the Jupyter notebook web application creates an environment which facilitates collaborative development of biomedical image analysis workflows. In this course, we will use a hands-on approach utilizing Python based SimpleITK Jupyter notebooks to explore and experiment with various toolkit features. Participants will follow along using their personal laptops, enabling them to explore the effects of changes and settings not covered by the instructor. We start by introducing the toolkit’s two basic data elements, Images and Transformations. We then combine the two, illustrating how to perform image resampling. Having mastered the concept of resampling, we show how to use SimpleITK as a tool for image preparation and data augmentation for deep learning via spatial and intensity transformations. We then turn our focus to the toolkit’s registration framework, exploring various components including: optimizer selection, the use of linear and deformable transformations, the embedded multi-resolution framework, self-calibrating optimizers and the use of callbacks for registration progress monitoring. Finally, we illustrate the use of a variety of SimpleITK filters to implement an image analysis workflow that includes segmentation and shape analysis.
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