A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies

Onur Ganiler, Arnau Oliver, Santiago Diez Donoso, Jordi Freixenet, Joan C. Vilanova, Brigitte Beltran, Lluís Ramió-Torrentà, Àlex Rovira, Xavier Lladó

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)


Introduction: 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. Conclusion: 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.

Original languageEnglish
Pages (from-to)363-374
Number of pages12
Issue number5
Publication statusPublished - 2014 Jan 1


  • 3D subtraction
  • Brain MRI longitudinal analysis
  • Lesion change detection
  • Multiple sclerosis

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine


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