A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
Name
1803.07682.pdf
Description
Submitted version
Size
1 MB
Format
Adobe PDF
Checksum (MD5)
2b09fae670f5cc087dcf0ac15aca4fee
Author(s)
Luo, Jie
Toews, Matthew
Machado, Ines
Frisken, Sarah
Zhang, Miaomiao
Preiswerk, Frank
Sedghi, Alireza
Ding, Hongyi
Pieper, Steve
Golland, Polina
Date Issued
2018
Publisher
Springer International Publishing
Citation
Luo, Jie, Toews, Matthew, Machado, Ines, Frisken, Sarah, Zhang, Miaomiao et al. 2018. "A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation."
Version
Original manuscript
Abstract
© 2018, Springer Nature Switzerland AG. 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-030-00937-3_4
