title: Reproducible segmentation of white matter hyperintensities using a new statistical definition.
abstract: OBJECTIVE
  We present a method based on a proposed statistical definition of white matter hyperintensities (WMH), which can work with any combination of conventional magnetic resonance (MR) sequences without depending on manually delineated samples.
  
  
  METHODS
  T1-weighted, T2-weighted, FLAIR, and PD sequences acquired at 1.5 Tesla from 119 subjects from the Kings Health Partners-Dementia Case Register (healthy controls, mild cognitive impairment, Alzheimer's disease) were used. The segmentation was performed using a proposed definition for WMH based on the one-tailed Kolmogorov-Smirnov test.
  
  
  RESULTS
  The presented method was verified, given all possible combinations of input sequences, against manual segmentations and a high similarity (Dice 0.85-0.91) was observed. Comparing segmentations with different input sequences to one another also yielded a high similarity (Dice 0.83-0.94) that exceeded intra-rater similarity (Dice 0.75-0.91). We compared the results with those of other available methods and showed that the segmentation based on the proposed definition has better accuracy and reproducibility in the test dataset used.
  
  
  CONCLUSIONS
  Overall, the presented definition is shown to produce accurate results with higher reproducibility than manual delineation. This approach can be an alternative to other manual or automatic methods not only because of its accuracy, but also due to its good reproducibility.
authors.affiliation: Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Hälsovägen 7, Huddinge, 14157, Stockholm, Sweden. soheil.damangir@ki.se.
authors.forename: Soheil
authors.identifier: http://orcid.org/0000-0001-5425-5859
authors.initials: S
authors.lastname: Damangir
authors.affiliation: Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Hälsovägen 7, Huddinge, 14157, Stockholm, Sweden.
authors.forename: Eric
authors.initials: E
authors.lastname: Westman
authors.affiliation: Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Hälsovägen 7, Huddinge, 14157, Stockholm, Sweden.|Institute of Psychiatry, King's College London, Box P089, De Crespigny Park, London, SE5 8AF, UK.
authors.forename: Andrew
authors.initials: A
authors.lastname: Simmons
authors.affiliation: Department of Physics and Medical Technology, VU University Medical Center, De Boelelaan 1118, 1081HZ, Amsterdam, The Netherlands.|Department of Radiology and Nuclear Medicine, VU University Medical Center, De Boelelaan 1118, 1081HZ, Amsterdam, The Netherlands.
authors.forename: Hugo
authors.initials: H
authors.lastname: Vrenken
authors.affiliation: Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Hälsovägen 7, Huddinge, 14157, Stockholm, Sweden.
authors.forename: Lars-Olof
authors.initials: LO
authors.lastname: Wahlund
authors.affiliation: Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Hälsovägen 7, Huddinge, 14157, Stockholm, Sweden.
authors.forename: Gabriela
authors.initials: G
authors.lastname: Spulber
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D056784:Leukoencephalopathies / Q000000981:diagnostic imaging* / Q000473:pathology; D000069550:Machine Learning; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D066127:White Matter / Q000000981:diagnostic imaging* / Q000473:pathology
keywords: Multimodal segmentation; Segmentation; White matter hyperintensities; White matter lesion