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Erschienen in: International Journal of Computer Assisted Radiology and Surgery 10/2018

04.06.2018 | Original Article

Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching

verfasst von: Inês Machado, Matthew Toews, Jie Luo, Prashin Unadkat, Walid Essayed, Elizabeth George, Pedro Teodoro, Herculano Carvalho, Jorge Martins, Polina Golland, Steve Pieper, Sarah Frisken, Alexandra Golby, William Wells III

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 10/2018

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Abstract

Purpose

The brain undergoes significant structural change over the course of neurosurgery, including highly nonlinear deformation and resection. It can be informative to recover the spatial mapping between structures identified in preoperative surgical planning and the intraoperative state of the brain. We present a novel feature-based method for achieving robust, fully automatic deformable registration of intraoperative neurosurgical ultrasound images.

Methods

A sparse set of local image feature correspondences is first estimated between ultrasound image pairs, after which rigid, affine and thin-plate spline models are used to estimate dense mappings throughout the image. Correspondences are derived from 3D features, distinctive generic image patterns that are automatically extracted from 3D ultrasound images and characterized in terms of their geometry (i.e., location, scale, and orientation) and a descriptor of local image appearance. Feature correspondences between ultrasound images are achieved based on a nearest-neighbor descriptor matching and probabilistic voting model similar to the Hough transform.

Results

Experiments demonstrate our method on intraoperative ultrasound images acquired before and after opening of the dura mater, during resection and after resection in nine clinical cases. A total of 1620 automatically extracted 3D feature correspondences were manually validated by eleven experts and used to guide the registration. Then, using manually labeled corresponding landmarks in the pre- and post-resection ultrasound images, we show that our feature-based registration reduces the mean target registration error from an initial value of 3.3 to 1.5 mm.

Conclusions

This result demonstrates that the 3D features promise to offer a robust and accurate solution for 3D ultrasound registration and to correct for brain shift in image-guided neurosurgery.
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Metadaten
Titel
Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching
verfasst von
Inês Machado
Matthew Toews
Jie Luo
Prashin Unadkat
Walid Essayed
Elizabeth George
Pedro Teodoro
Herculano Carvalho
Jorge Martins
Polina Golland
Steve Pieper
Sarah Frisken
Alexandra Golby
William Wells III
Publikationsdatum
04.06.2018
Verlag
Springer International Publishing
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 10/2018
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-018-1786-7

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