Registration of vascular structures using a hybrid mixture model

Bayer S, Zhai Z, Strumia M, Tong X, Gao Y, Staring M, Stoel B, Fahrig R, Arya N, Maier A, Ravikumar N (2019)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2019

Journal

International Journal of Computer Assisted Radiology and Surgery Springer Verlag (Germany)

Pages Range: 1–10

URI: https://link.springer.com/article/10.1007/s11548-019-02007-y

DOI: 10.1007/s11548-019-02007

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%" role="presentation">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.

Authors with CRIS profile

Siming Bayer Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Nishant Ravikumar Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung)

Related research project(s)

Intraoperative brain shift compensation and point-based vascular registration May 1, 2016 - Oct. 31, 2019

Involved external institutions

Nankai University (NKU) / 南开大学 CN China (CN) Leiden University Medical Center NL Netherlands (NL) Klinikum Region Hannover DE Germany (DE) Siemens Ltd., China CN China (CN) Siemens Healthineers DE Germany (DE)

How to cite

APA:

Bayer, S., Zhai, Z., Strumia, M., Tong, X., Gao, Y., Staring, M.,... Ravikumar, N. (2019). Registration of vascular structures using a hybrid mixture model. International Journal of Computer Assisted Radiology and Surgery, 1–10. https://doi.org/10.1007/s11548-019-02007

MLA:

Bayer, Siming, et al. "Registration of vascular structures using a hybrid mixture model." International Journal of Computer Assisted Radiology and Surgery (2019): 1–10.

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