title: Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease.
abstract: OBJECTIVE
  White matter hyperintensities (WMH) are seen on FLAIR-MRI in several neurological disorders, including multiple sclerosis, dementia, Parkinsonism, stroke and cerebral small vessel disease (SVD). WMHs are often used as biomarkers for prognosis or disease progression in these diseases, and additionally longitudinal quantification of WMHs is used to evaluate therapeutic strategies. Human readers show considerable disagreement and inconsistency on detection of small lesions. A multitude of automated detection algorithms for WMHs exists, but since most of the current automated approaches are tuned to optimize segmentation performance according to Jaccard or Dice scores, smaller WMHs often go undetected in these approaches. In this paper, the authors propose a method to accurately detect all WMHs, large as well as small.
  
  
  METHODS
  A two-stage learning approach was used to discriminate WMHs from normal brain tissue. Since small and larger WMHs have quite a different appearance, the authors have trained two probabilistic classifiers: one for the small WMHs (⩽3 mm effective diameter) and one for the larger WMHs (>3 mm in-plane effective diameter). For each size-specific classifier, an Adaboost is trained for five iterations, with random forests as the basic classifier. The feature sets consist of 22 features including intensities, location information, blob detectors, and second order derivatives. The outcomes of the two first-stage classifiers were combined into a single WMH likelihood by a second-stage classifier. Their method was trained and evaluated on a dataset with MRI scans of 362 SVD patients (312 subjects for training and validation annotated by one and 50 for testing annotated by two trained raters). To analyze performance on the separate test set, the authors performed a free-response receiving operating characteristic (FROC) analysis, instead of using segmentation based methods that tend to ignore the contribution of small WMHs.
  
  
  RESULTS
  Experimental results based on FROC analysis demonstrated a close performance of the proposed computer aided detection (CAD) system to human readers. While an independent reader had 0.78 sensitivity with 28 false positives per volume on average, their proposed CAD system reaches a sensitivity of 0.73 with the same number of false positives.
  
  
  CONCLUSIONS
  The authors have developed a CAD system with all its ingredients being optimized for a better detection of WMHs of all size, which shows performance close to an independent reader.
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525, The Netherlands and Institute for Computing and Information Sciences, Radboud University, Nijmegen 6525 GA, The Netherlands.
authors.forename: Mohsen
authors.initials: M
authors.lastname: Ghafoorian
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525, The Netherlands.
authors.forename: Nico
authors.initials: N
authors.lastname: Karssemeijer
authors.affiliation: Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen 6525 EN, The Netherlands.
authors.forename: Inge W M
authors.initials: IW
authors.lastname: van Uden
authors.affiliation: Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Radboud University Medical Center, Nijmegen 6525 EN, The Netherlands.
authors.forename: Frank-Erik
authors.initials: FE
authors.lastname: de Leeuw
authors.affiliation: Institute for Computing and Information Sciences, Radboud University, Nijmegen 6525 EC, The Netherlands.
authors.forename: Tom
authors.initials: T
authors.lastname: Heskes
authors.affiliation: Institute for Computing and Information Sciences, Radboud University, Nijmegen 6525 EC, The Netherlands.
authors.forename: Elena
authors.initials: E
authors.lastname: Marchiori
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525, The Netherlands.
authors.forename: Bram
authors.initials: B
authors.lastname: Platel
mesh_terms: D001331:Automation; D059345:Cerebral Small Vessel Diseases / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D066127:White Matter / Q000000981:diagnostic imaging*