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2018 | OriginalPaper | Chapter

A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation

Authors : Jie Luo, Matthew Toews, Ines Machado, Sarah Frisken, Miaomiao Zhang, Frank Preiswerk, Alireza Sedghi, Hongyi Ding, Steve Pieper, Polina Golland, Alexandra Golby, Masashi Sugiyama, William M. Wells III

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Publisher: Springer International Publishing

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Abstract

A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Developing such a registration method is very challenging, due to factors such as the tumor resection, the complexity of brain pathology and the demand for fast computation. We propose a novel feature-driven active registration framework. Here, landmarks and their displacement are first estimated from a pair of US images using corresponding local image features. Subsequently, a Gaussian Process (GP) model is used to interpolate a dense deformation field from the sparse landmarks. Kernels of the GP are estimated by using variograms and a discrete grid search method. If necessary, the user can actively add new landmarks based on the image context and visualization of the uncertainty measure provided by the GP to further improve the result. We retrospectively demonstrate our registration framework as a robust and accurate brain shift compensation solution on clinical data.

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Title: A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
Authors: Jie Luo
Matthew Toews
Ines Machado
Sarah Frisken
Miaomiao Zhang
Frank Preiswerk
Alireza Sedghi
Hongyi Ding
Steve Pieper
Polina Golland
Alexandra Golby
Masashi Sugiyama
William M. Wells III
Publisher: Springer International Publishing
Book: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Print ISBN: 978-3-030-00936-6

Electronic ISBN: 978-3-030-00937-3

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00937-3_4

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