Evaluation of three automatic brain vessel segmentation methods for stereotactical trajectory planning
Introduction
Stereotactical procedures such as brain biopsy (frame-based and frameless) or placement of electrodes for deep-brain stimulation (DBS) require exact trajectory planning to maximize effectiveness while minimizing morbidity and mortality. Consequently, avoidance of blood vessels in the trajectory path constitutes an indispensable part of procedure planning to prevent bleeding and ischemic complications.
Steady innovation in magnetic resonance imaging (MRI) is leading to continuously increasing spatial resolution and image contrast, whereby more and more smaller vessels can be visualized, making trajectory planning, which is still performed manually by examination of multi-planar reconstructions of image data, even more complex. Consequently, there is a growing demand for advanced stereotactical planning tools. These software systems should be capable of detecting blood vessels in the trajectory path and guide the surgeon to stay clear of such vessels.
Robust vessel segmentation is a cornerstone of any such planning system and many vessel enhancement algorithms have been evaluated using time-of-flight (TOF) magnetic resonance angiography (MRA) or CT angiography (CTA) images [1], [2], [3].
Although both imaging modalities are vessel-specific and thus arguably represent the best source imaging for vessel detection, they require time (MRA) or considerable radiation and contrast-agent exposure (CTA). These apparent drawbacks may explain the fact that such imaging protocols are not part of routine stereotactical imaging and that vessel detection is not part of contemporary stereotactical planning software yet.
Conversely, contrast-enhanced T1-weighted (T1CE) MRI sequences are an indispensable component of any MRI used in stereotactical planning. Given that this data is readily available for almost every stereotactic procedure, the question arises if robust vessel detection is possible with this imaging modality.
We have developed a software application that incorporates three vessel segmentation algorithms to detect brain vessels from T1CE and MRA images. The system is designed to generate warnings if minimum-distance constraints between vasculature and trajectory are violated. The software application was developed in C++ using the Qt Toolkit, The Visualization Toolkit (VTK) [4] and the Insight Segmentation and Registration Toolkit (ITK) [5], [6].
Vessel segmentation was performed by the combination of a generalized multiscale objectness filter [7] (derived from the vesselness filter of Frangi [8]) with subsequent level-set development of the vessel boundary towards the vessel edges.
The objective of this pilot study was to evaluate the performance of this software in 45 stereotactic trajectories. Segmentation results in T1CE and 3T or 7T MRA were compared to gold-standard manual segmentation by human experts. Furthermore, the accuracy of collision warnings in a clinical setting was evaluated.
Section snippets
Study population
Six brain tumor patients (four males and two females) scheduled for stereotactic biopsy, were included in this study. Additionally, image data from three healthy volunteers (2 males and 1 female) examined using ultra-high-field 7T MRI were collected.
Ethics approval
The study was performed in accordance with the declaration of Helsinki. Design of this study, image acquisition using optimized MR sequences, collection and analysis of patient data were approved by the institutional review board of the University
Processing time
Computation time was measured on an Intel® Core i7-3770 (4 [email protected] GHz) running FreeBSD-11.2 (Table 2). As expected for the Vesselness and FastMarching methods, linear regression analysis revealed a strong linear correlation between total computation time and the number of voxels in the bounding box of the trajectory.
The application of the LevelSet method led to a three-fold increase of processing time compared to the other methods with an average of 17.6 [16.9, 18.4] seconds. Depending on
Discussion
The results of our analysis support our hypothesis that meaningful automatic vessel collision warning for stereotactic planning is feasible. The combination of a vessel enhancement filter [8], [12] with subsequent local level-set segmentation as in the FastMarching and LevelSet methods performed significantly better than vessel enhancement filtering alone.
Furthermore, we could show that dedicated vessel imaging (MRA) does not seem to be required if high-quality T1CE images are available. Many
Conclusions
From the data of our pilot study, we conclude that it is feasible to integrate a clinically meaningful vessel detection and collision warning system into stereotactical planning software. Such a system is capable of operating on either MRA or T1CE images.
Declaration of Competing Interest
All authors declare that no conflicts of interest do exist.
Acknowledgements
We thank Prof. Dr. Armin Nagel for kindly providing the 7T-TOF image data acquired at the German Cancer Research Center, Heidelberg.
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