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Detection of longitudinal changes in brain structures is a common clinical task when assessing the progress of cerebrovascular and neurodegenerative diseases, which manifest in appearing and disappearing white matter lesions (WMLs). Changes of WMLs are usually quanti ed by their manual outlines and compared across longi- tudinal, serial magnetic resonance (MR) brain images. Since manual outlining in 3D MR images is subjective and inaccurate, several automated methods were proposed so as to enhance the sensitivity, reliability and re- peatability of change detection of WMLs. However, the absence of publicly available synthetic or clinical MR image databases with corresponding ground truth of changes renders the validation and comparison of any new and existing automated methods highly subjective. In this paper, we focus on the validation and comparison of three state-of-the-art intensity based methods for detection of longitudinal changes of WMLs. To objectively assess the three methods we created several synthetic MR image databases using a generative lesion model, which was trained on manually outlined patches of WMLs in a clinical MR image database of 22 patients. Val- idation was also performed on clinical MR image database of MS patients. Performances of the three change detection methods were evaluated by computing the similarity index and sensitivity between the obtained and the ground truth binary change map. The obtained similarity indices were in the range of 0.40-0.77, which should be improved for clinical use, while the comparison of methods revealed that the intensity subtraction method achieved similar performance as the change vector analysis method, which employed two MR sequences for change detection. Third method was based on local steering kernels and exhibited stable performance both on synthetic and clinical MR image databases.
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