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"Registration of vascular structures using a hybrid mixture model"

Siming Bayer, Zhiwei Zhai, Maddalena Strumia, Xiaoguang Tong, Ying Gao, Marius Staring, Berend Stoel, Rebecca Fahrig, Arya Nabavi, Andreas Maier and Nishant Ravikumar

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 (CPD) and Student's t-Distribution mixture model (TMM), 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.

 

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Copyright © 2019 by the authors. Published version © 2019 by Springer. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

 

BibTeX entry

@article{Bayer:2019,
author = "{Siming Bayer and Zhiwei Zhai and Maddalena Strumia and Xiaoguang Tong and Ying Gao and Marius Staring and Berend Stoel and Rebecca Fahrig and Arya Nabavi and Andreas Maier and Nishant Ravikumar}",
title = "{Registration of vascular structures using a hybrid mixture model}",
journal = "{International Journal of Computer Assisted Radiology and Surgery}",
year = "{2019}",
}

last modified: 11-06-2019 |webmaster |Copyright 2004-2019 © by Marius Staring