期刊
EUROPEAN RADIOLOGY EXPERIMENTAL
卷 5, 期 1, 页码 -出版社
SPRINGERNATURE
DOI: 10.1186/s41747-020-00200-2
关键词
Average Hausdorff distance; Cerebral angiography; Cerebral arteries; Image processing (computer-assisted)
资金
- German Federal Ministry of Education and Research through grant Centre for Stroke Research Berlin
- German Federal Ministry of Education and Research through Go-Bio grant for the research group PREDICTioN2020
- German Research Foundation (DFG) via the Open Access Publication Fund of Charite Universitatsmedizin Berlin
- Projekt DEAL
The average Hausdorff distance is commonly used to calculate the distance between two point sets, but suffers from ranking errors in medical image segmentation applications. To address this issue, a balanced average Hausdorff distance measure is proposed, showing higher correlation and suitability for rankings and quality assessment of segmentations.
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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