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A longitudinal WMH segmentation algorithm is proposed.Time points segmentation are constrained by an average data model.It is shown to be unbiased to time point ordering.A longitudinal lesion simulator is developed for validation.The longitudinal solution is more robust compared to cross-sectional methods.Although white matter hyperintensities evolve in the course of ageing, few solutions exist to consider the lesion segmentation problem longitudinally. Based on an existing automatic lesion segmentation algorithm, a longitudinal extension is proposed. For evaluation purposes, a longitudinal lesion simulator is created allowing for the comparison between the longitudinal and the cross-sectional version in various situations of lesion load progression. Finally, applied to clinical data, the proposed framework demonstrates an increased robustness compared to available cross-sectional methods and findings are aligned with previously reported clinical patterns.