Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review. (Articolo in rivista)

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Label
  • Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review. (Articolo in rivista) (literal)
Anno
  • 2015-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007%2Fs12021-015-9260-y (literal)
Alternative label
  • Maria Eugenia Caligiuri 1, Paolo Perrotta 1, Antonio Augimeri 1, Federico Rocca 1, Aldo Quattrone 1,2, Andrea Cherubini 1 (2015)
    Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review.
    in Neuroinformatics
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Maria Eugenia Caligiuri 1, Paolo Perrotta 1, Antonio Augimeri 1, Federico Rocca 1, Aldo Quattrone 1,2, Andrea Cherubini 1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Pubblicato on-line: 5 Febbraio 2015 (literal)
Rivista
Note
  • PubMe (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1. Neuroimaging Unit, Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Germaneto (CZ) Italy. 2. Institute of Neurology, University BMagna Graecia, Germaneto (CZ) Italy (literal)
Titolo
  • Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review. (literal)
Abstract
  • White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular disorders. A truly reliable and fully automated method for quantitative assessment of WMH on magnetic resonance imaging (MRI) has not yet been identified. In this paper, we review and compare the large number of automated approaches proposed for segmentation of WMH in the elderly and in patients with vascular risk factors. We conclude that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives. All these features should not exclude computational leanness and adaptability to available data. (literal)
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