title: Validation of White-Matter Lesion Change Detection Methods on a Novel Publicly Available MRI Image Database.
abstract: Changes of white-matter lesions (WMLs) are good predictors of the progression of neurodegenerative diseases like multiple sclerosis (MS). Based on longitudinal magnetic resonance (MR) imaging the changes can be monitored, while the need for their accurate and reliable quantification led to the development of several automated MR image analysis methods. However, an objective comparison of the methods is difficult, because publicly unavailable validation datasets with ground truth and different sets of performance metrics were used. In this study, we acquired longitudinal MR datasets of 20 MS patients, in which brain regions were extracted, spatially aligned and intensity normalized. Two expert raters then delineated and jointly revised the WML changes on subtracted baseline and follow-up MR images to obtain ground truth WML segmentations. The main contribution of this paper is an objective, quantitative and systematic evaluation of two unsupervised and one supervised intensity based change detection method on the publicly available datasets with ground truth segmentations, using common pre- and post-processing steps and common evaluation metrics. Besides, different combinations of the two main steps of the studied change detection methods, i.e. dissimilarity map construction and its segmentation, were tested to identify the best performing combination.
authors.affiliation: University of Ljubljana, Faculty of Electrical Engineering, Tržaška 25, 1000, Ljubljana, Slovenia. ziga.lesjak@fe.uni-lj.si.
authors.forename: Žiga
authors.initials: Ž
authors.lastname: Lesjak
authors.affiliation: University of Ljubljana, Faculty of Electrical Engineering, Tržaška 25, 1000, Ljubljana, Slovenia.
authors.forename: Franjo
authors.initials: F
authors.lastname: Pernuš
authors.affiliation: University of Ljubljana, Faculty of Electrical Engineering, Tržaška 25, 1000, Ljubljana, Slovenia.|Computer Vision Systems, Sensum, Tehnološki Park 21, 1000, Ljubljana, Slovenia.
authors.forename: Boštjan
authors.initials: B
authors.lastname: Likar
authors.affiliation: University of Ljubljana, Faculty of Electrical Engineering, Tržaška 25, 1000, Ljubljana, Slovenia.|Computer Vision Systems, Sensum, Tehnološki Park 21, 1000, Ljubljana, Slovenia.
authors.forename: Žiga
authors.initials: Ž
authors.lastname: Špiclin
mesh_terms: D000328:Adult; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000000981:diagnostic imaging / Q000473:pathology; D012372:ROC Curve; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D066127:White Matter / Q000000981:diagnostic imaging* / Q000473:pathology*; D055815:Young Adult
keywords: Change detection; Image segmentation; Lesion; Magnetic resonance; Multiple sclerosis; Quantitative evaluation; Validation dataset