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  On the usage of average Hausdorff distance for segmentation performance assessment: Hidden error when used for ranking

Aydin, O. U., Taha, A. A., Hilbert, A., Khalil, A., Galinovic, I., Fiebach, J. B., et al. (2021). On the usage of average Hausdorff distance for segmentation performance assessment: Hidden error when used for ranking. European Radiology Experimental, 5: 4. doi:10.1186/s41747-020-00200-2.

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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 Istvan1, 6, Author
Affiliations:
1Charité Lab for Artificial Intelligence in Medicine (CLAIM), Charité University Medicine Berlin, Germany, ou_persistent22              
2Research 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              
6 School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, University of Birmingham, United Kingdom, ou_persistent22              

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Free keywords: Average Hausdorff distance; Cerebral angiography; Cerebral arteries; Image processing (computer-assisted)
 Abstract: Average Hausdorff distance is a widely used performance measure to calculate the distance between two point sets. In medical image segmentation, it is used to compare ground truth images with segmentations allowing their ranking. We identified, however, ranking errors of average Hausdorff distance making it less suitable for applications in segmentation performance assessment. To mitigate this error, we present a modified calculation of this performance measure that we have coined “balanced average Hausdorff distance”. To simulate segmentations for ranking, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation as our use-case. Adding the created errors consecutively and randomly to the ground truth, we created sets of simulated segmentations with increasing number of errors. Each set of simulated segmentations was ranked using both performance measures. We calculated the Kendall rank correlation coefficient between the segmentation ranking and the number of errors in each simulated segmentation. The rankings produced by balanced average Hausdorff distance had a significantly higher median correlation (1.00) than those by average Hausdorff distance (0.89). In 200 total rankings, the former misranked 52 whilst the latter misranked 179 segmentations. Balanced average Hausdorff distance is more suitable for rankings and quality assessment of segmentations than average Hausdorff distance.

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Language(s): eng - English
 Dates: 2020-09-122020-12-032021-01-21
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1186/s41747-020-00200-2
PMID: 33474675
PMC: PMC7817746
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Title: European Radiology Experimental
Source Genre: Journal
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Publ. Info: Cham : Springer
Pages: - Volume / Issue: 5 Sequence Number: 4 Start / End Page: - Identifier: ISSN: 2509-9280
CoNE: https://pure.mpg.de/cone/journals/resource/2509-9280