Bifurcation matching for consistent cerebral vessel labeling in CTA of stroke patients

Rist L, Taubmann O, Thamm F, Ditt H, Sühling M, Maier A (2022)

Publication Type: Journal article

Publication year: 2022

Journal

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

Original Authors: Leonhard Rist, Oliver Taubmann, Florian Thamm, Hendrik Ditt, Michael Sühling, Andreas Maier

DOI: 10.1007/s11548-022-02750-9

Abstract

Purpose

Vessel labeling is a prerequisite for comparing cerebral vasculature across patients, e.g., for straightened vessel examination or for localization. Extracting vessels from computed tomography angiography scans may come with a trade-off in segmentation accuracy. Vessels might be neglected or artificially created, increasing the difficulty of labeling. Related work mainly focuses on magnetic resonance angiography without stroke and uses trainable approaches requiring costly labels.


Methods

We present a robust method to identify major arteries and bifurcations in cerebrovascular models generated from existing segmentations. To localize bifurcations of the Circle of Willis, candidate paths for the adjacent vessels of interest are identified using registered landmarks. From those paths, the optimal ones are extracted by recursively maximizing an objective function for all adjacent vessels starting from a bifurcation to avoid erroneous paths and compensate for stroke.


Results

In 100 CTA stroke data sets for evaluation, 6 bifurcation locations are placed correctly in 85% of cases; 92.5% when allowing a margin of 5 mm. On average, 14 vessels of interest are found in 90% of the cases and traced correctly end-to-end in 73.5%. The baseline achieves similar detection rates but only 35.5% of the arteries are traced in full.


Conclusion

Formulating the vessel labeling process as a maximization task for bifurcation matching can vastly improve accurate vessel tracing. The proposed algorithm only uses simple features and does not require expensive training data.

Authors with CRIS profile

Leonhard Rist Lehrstuhl für Informatik 5 (Mustererkennung) Florian Thamm Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens Healthineers DE Germany (DE) Siemens AG, Healthcare Sector DE Germany (DE)

How to cite

APA:

Rist, L., Taubmann, O., Thamm, F., Ditt, H., Sühling, M., & Maier, A. (2022). Bifurcation matching for consistent cerebral vessel labeling in CTA of stroke patients. International Journal of Computer Assisted Radiology and Surgery. https://dx.doi.org/10.1007/s11548-022-02750-9

MLA:

Rist, Leonhard, et al. "Bifurcation matching for consistent cerebral vessel labeling in CTA of stroke patients." International Journal of Computer Assisted Radiology and Surgery (2022).

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