title: A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies.
abstract: BACKGROUND
  Time-series analysis of magnetic resonance images (MRI) is of great value for multiple sclerosis (MS) diagnosis and follow-up. In this paper, we present an unsupervised subtraction approach which incorporates multisequence information to deal with the detection of new MS lesions in longitudinal studies.
  
  
  METHODS
  The proposed pipeline for detecting new lesions consists of the following steps: skull stripping, bias field correction, histogram matching, registration, white matter masking, image subtraction, automated thresholding, and postprocessing. We also combine the results of PD-w and T2-w images to reduce false positive detections.
  
  
  RESULTS
  Experimental tests are performed in 20 MS patients with two temporal studies separated 12 (12M) or 48 (48M) months in time. The pipeline achieves very good performance obtaining an overall sensitivity of 0.83 and 0.77 with a false discovery rate (FDR) of 0.14 and 0.18 for the 12M and 48M datasets, respectively. The most difficult situation for the pipeline is the detection of very small lesions where the obtained sensitivity is lower and the FDR higher.
  
  
  CONCLUSIONS
  Our fully automated approach is robust and accurate, allowing detection of new appearing MS lesions. We believe that the pipeline can be applied to large collections of images and also be easily adapted to monitor other brain pathologies.
authors.affiliation: VICOROB Computer Vision and Robotics Group, University of Girona, Edifici P-IV. Campus de Montilivi s/n, 17071, Girona, Spain, onur.ganiler@udg.edu.
authors.forename: Onur
authors.initials: O
authors.lastname: Ganiler
authors.forename: Arnau
authors.initials: A
authors.lastname: Oliver
authors.forename: Yago
authors.initials: Y
authors.lastname: Diez
authors.forename: Jordi
authors.initials: J
authors.lastname: Freixenet
authors.forename: Joan C
authors.initials: JC
authors.lastname: Vilanova
authors.forename: Brigitte
authors.initials: B
authors.lastname: Beltran
authors.forename: Lluís
authors.initials: L
authors.lastname: Ramió-Torrentà
authors.forename: Alex
authors.initials: A
authors.lastname: Rovira
authors.forename: Xavier
authors.initials: X
authors.lastname: Lladó
mesh_terms: D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000175:diagnosis*