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Quantification of white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Advanced measurements are obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (http://wmh.isi.uu.nl/). Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations. A secret test set of 110 images from five MR scanners was used for evaluation. Methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice Similarity Coefficient, (2) modified Hausdorff distance (95th percentile), (3) absolute percentage volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner robustness. Twenty participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods. There is one clear winner, which also has the best inter-scanner robustness. The challenge remains open for future submissions and provides a public platform for method evaluation.

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