Bayer, S, Zhai, Z, Strumia, M et al. (8 more authors) (2019) Registration of vascular structures using a hybrid mixture model. International Journal of Computer Assisted Radiology and Surgery, 14 (9). pp. 1507-1516. ISSN 1861-6410
Abstract
Purpose: Morphological changes to anatomy resulting from invasive surgical procedures or pathology, typically alter the surrounding vasculature. This makes it useful as a descriptor for feature-driven image registration in various clinical applications. However, registration of vasculature remains challenging, as vessels often differ in size and shape, and may even miss branches, due to surgical interventions or pathological changes. Furthermore, existing vessel registration methods are typically designed for a specific application. To address this limitation, we propose a generic vessel registration approach useful for a variety of clinical applications, involving different anatomical regions.
Methods: A probabilistic registration framework based on a hybrid mixture model, with a refinement mechanism to identify missing branches (denoted as HdMM+) during vasculature matching, is introduced. Vascular structures are represented as 6-dimensional hybrid point sets comprising spatial positions and centerline orientations, using Student’s t-distributions to model the former and Watson distributions for the latter.
Results: The proposed framework is evaluated for intraoperative brain shift compensation, and monitoring changes in pulmonary vasculature resulting from chronic lung disease. Registration accuracy is validated using both synthetic and patient data. Our results demonstrate, HdMM+ is able to reduce more than 85% of the initial error for both applications, and outperforms the state-of-the-art point-based registration methods such as coherent point drift and Student’s t-distribution mixture model, in terms of mean surface distance, modified Hausdorff distance, Dice and Jaccard scores.
Conclusion: The proposed registration framework models complex vascular structures using a hybrid representation of vessel centerlines, and accommodates intricate variations in vascular morphology. Furthermore, it is generic and flexible in its design, enabling its use in a variety of clinical applications.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © CARS 2019. This is a post-peer-review, pre-copyedit version of an article published in International Journal of Computer Assisted Radiology and Surgery. The final authenticated version is available online at: https://doi.org/10.1007/s11548-019-02007-y. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Non-rigid registration; Point matching; Brain shift; Pulmonary vascular diseases |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Aug 2019 10:33 |
Last Modified: | 07 Jun 2020 00:38 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/s11548-019-02007-y |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149277 |