Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding

White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factors and neurodegenerative diseases affect lesion loa...

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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Jg. 202; S. 116056
Hauptverfasser:	Sundaresan, Vaanathi, Zamboni, Giovanna, Le Heron, Campbell, Rothwell, Peter M., Husain, Masud, Battaglini, Marco, De Stefano, Nicola, Jenkinson, Mark, Griffanti, Ludovica
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 15.11.2019 Elsevier Limited Academic Press
Schlagworte:	Age Age Factors Aged Aged, 80 and over Aging Algorithms Artificial intelligence Automation Brain Diseases - diagnostic imaging Classification Datasets Female Humans Image Interpretation, Computer-Assisted - methods Learning algorithms Lesion probability map Lesion segmentation Lesions Leukoencephalopathy Load Machine Learning Magnetic Resonance Imaging - methods Male Methods Middle Aged Neural networks Neurodegenerative diseases Neuroimaging - methods Pattern Recognition, Automated - methods Population Probability Risk factors Segmentation Spatial distribution Structural MRI Substantia alba Thresholding Vascular diseases White Matter - diagnostic imaging White matter hyperintensities Structural MRI Lesion probability map White matter hyperintensities Lesion segmentation Thresholding Machine learning
ISSN:	1053-8119, 1095-9572, 1095-9572
Online-Zugang:	Volltext
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