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Quantification of cerebral white matter hyperintensities (WMH) of presumedvascular origin is of key importance in many neurological research studies.Currently, measurements are often still obtained from manual segmentations onbrain MR images, which is a laborious procedure. Automatic WMH segmentationmethods exist, but a standardized comparison of the performance of such methodsis lacking. We organized a scientific challenge, in which developers couldevaluate their method on a standardized multi-center/-scanner image dataset,giving an objective comparison: the WMH Segmentation Challenge(https://wmh.isi.uu.nl/).  Sixty T1+FLAIR images from three MR scanners were released with manual WMHsegmentations for training. A test set of 110 images from five MR scanners wasused for evaluation. Segmentation methods had to be containerized and submittedto the challenge organizers. Five evaluation metrics were used to rank themethods: (1) Dice similarity coefficient, (2) modified Hausdorff distance (95thpercentile), (3) absolute log-transformed volume difference, (4) sensitivityfor 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 paperprovides a detailed analysis of the results. In brief, there is a cluster offour methods that rank significantly better than the other methods, with oneclear winner. The inter-scanner robustness ranking shows that not all methodsgeneralize to unseen scanners.  The challenge remains open for future submissions and provides a publicplatform for method evaluation.

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge