| Summary: | Virchow-Robin Spaces surround the perforating arteries of the brain and sometimes they become dilated. Studies suggest that those structures are correlated with some conditions such as lacunar strokes, small vessel diseases, multiple sclerosis or even normal aging. However, the majority of those studies are based on the detection of those structures by a human expert, in some regions of interest, which is prone to the subjectivity of the person doing the task. Moreover, dilated Virchow-Robin Spaces may look similar to lacunar strokes, making them difficult to identify. Few works have been proposed on the computational detection of dilated Virchow-Robin Spaces. In this paper, we propose a fully automatic tool, capable of preprocessing the magnetic resonance images, extract the most relevant regions of interest and detect dilated Virchow-Robin Spaces. Such a tool may be useful to eliminate human subjectivity, but also to improve the reproducibility of the studies, leading to more reliable correlations. An application to visualize and count the detected structures was also built, with the aim of helping in a study of the correlation of lacunar strokes, Virchow-Robin Spaces and vascular dementia.
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