Abstract
White matter (WM) lesions are a phenomena perceived in magnetic resonance imaging (MRI) which is prevalent in many different brain pathologies, hence the general interest in automated methods for lesion segmentation (LS). We provide a short review of some commonly used state-of-the-art approaches. The article is focused on the machine learning techniques which researches use to construct semi- and fully-automated tools for LS. In addition, we mention the preprocessing steps, features extraction, LS databases and validation techniques.
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Chyzhyk, D., Graña, M., Ritter, G. (2016). Review of Automatic Segmentation Methods of White Matter Lesions on MRI Data. In: Chen, YW., Tanaka, S., Howlett, R., Jain, L. (eds) Innovation in Medicine and Healthcare 2016. InMed 2016. Smart Innovation, Systems and Technologies, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-319-39687-3_29
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DOI: https://doi.org/10.1007/978-3-319-39687-3_29
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