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  An evaluation of performance measures for arterial brain vessel segmentation

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.

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 Creators:
Aydin, Orhun Utku1, Author
Taha, Abdel Aziz2, Author
Hilbert, Adam1, Author
Khalil, Ahmed3, 4, 5, Author           
Galinovic, Ivana3, Author
Fiebach, Jochen B.3, Author
Frey, Dietmar1, Author
Madai, Vince Istvan6, 7, Author
Affiliations:
1Charité Lab for Artificial Intelligence in Medicine (CLAIM), Charité University Medicine Berlin, Germany, ou_persistent22              
2Research Studio Data Science, Research Studios Austria, Salzburg, Austria, ou_persistent22              
3Center for Stroke Research, Charité University Medicine Berlin, Germany, ou_persistent22              
4Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
5MindBrainBody Institute, Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              
6QUEST Center for Transforming Biomedical Research, Berlin Institute of Health (BIH), Germany, ou_persistent22              
7Faculty of Computing, Engineering and the Built Environment, School of Computing and Digital Technology, Birmingham City University, United Kingdom, ou_persistent22              

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Free keywords: Average Hausdorff distance; Cerebral arteries; Cerebral vessel segmentation; Dice; Image processing (computer-assisted); Ranking; Segmentation; Segmentation measures
 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.

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Language(s): eng - English
 Dates: 2021-03-232021-07-072021-07-16
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1186/s12880-021-00644-x
PMID: 34271876
PMC: PMC8283850
 Degree: -

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Project name : -
Grant ID : 777107
Funding program : Horizon 2020
Funding organization : European Commission

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Title: BMC Medical Imaging
Source Genre: Journal
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Publ. Info: London : BioMed Central
Pages: - Volume / Issue: 21 (1) Sequence Number: 113 Start / End Page: - Identifier: ISSN: 1471-2342
CoNE: https://pure.mpg.de/cone/journals/resource/1471-2342