English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

An evaluation of performance measures for arterial brain vessel segmentation

MPS-Authors
/persons/resource/persons215473

Khalil,  Ahmed
Center for Stroke Research, Charité University Medicine Berlin, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
MindBrainBody Institute, Berlin School of Mind and Brain, Humboldt University Berlin, Germany;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

Aydin_2021.pdf
(Publisher version), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Aydin, O. U., Taha, A. A., Hilbert, A., Khalil, A., Galinovic, I., Fiebach, J. B., et al. (2021). An evaluation of performance measures for arterial brain vessel segmentation. BMC Medical Imaging, 21(1): 113. doi:10.1186/s12880-021-00644-x.


Cite as: https://hdl.handle.net/21.11116/0000-0009-2570-F
Abstract
Background: Arterial brain vessel segmentation allows utilising clinically relevant information contained within the cerebral vascular tree. Currently, however, no standardised performance measure is available to evaluate the quality of cerebral vessel segmentations. Thus, we developed a performance measure selection framework based on manual visual scoring of simulated segmentation variations to find the most suitable measure for cerebral vessel segmentation.

Methods: To simulate segmentation variations, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation. In 10 patients, we generated a set of approximately 300 simulated segmentation variations for each ground truth image. Each segmentation was visually scored based on a predefined scoring system and segmentations were ranked based on 22 performance measures common in the literature. The correlation of visual scores with performance measure rankings was calculated using the Spearman correlation coefficient.

Results: The distance-based performance measures balanced average Hausdorff distance (rank = 1) and average Hausdorff distance (rank = 2) provided the segmentation rankings with the highest average correlation with manual rankings. They were followed by overlap-based measures such as Dice coefficient (rank = 7), a standard performance measure in medical image segmentation.

Conclusions: Average Hausdorff distance-based measures should be used as a standard performance measure in evaluating cerebral vessel segmentation quality. They can identify more relevant segmentation errors, especially in high-quality segmentations. Our findings have the potential to accelerate the validation and development of novel vessel segmentation approaches.