Automatic segmentation of white matter hyperintensities robust to multicentre acquisition and pathological variability

White matter hyperintensities (WMH), commonly seen on FLAIR images in elderly people, are a risk factor for dementia onset and have been associated with motor and cognitive deficits. We present here a method to fully automatically segment WMH from T1 and FLAIR images. Iterative steps of non linear diffusion followed by watershed segmentation were applied on FLAIR images until convergence. Diffusivity function and associated contrast parameter were carefully designed to adapt to WMH segmentation. It resulted in piecewise constant images with enhanced contrast between lesions and surrounding tissues. Selection of WMH areas was based on two characteristics: 1) a threshold automatically computed for intensity selection, 2) main location of areas in white matter. False positive areas were finally removed based on their proximity with cerebrospinal fluid/grey matter interface. Evaluation was performed on 67 patients: 24 with amnestic mild cognitive impairment (MCI), from five different centres, and 43 with Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoaraiosis (CADASIL) acquired in a single centre. Results showed excellent volume agreement with manual delineation (Pearson coefficient: r=0.97, p<0.001) and substantial spatial correspondence (Similarity Index: 72%±16%). Our method appeared robust to acquisition differences across the centres as well as to pathological variability.