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

Abstract

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.

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Notes

  1. 1.

    Xinapse Systems, JIM software webpage, http://www.xinapse.com/home.php .

  2. 2.

    BET is part of the public FSL software. http://www.fmrib.ox.ac.uk/analysis/research/bet/ .

  3. 3.

    The N4 algorithm is part of the ITK library http://www.itk.org/Doxygen/html/classitk_1_1N4BiasFieldCorrectionImageFilter.html .

  4. 4.

    This software is part of the ITK library http://www.itk.org/SimpleITKDoxygen/html/classitk_1_1simple_1_1HistogramMatchingImageFilter.html .

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We declare that we have no conflict of interest.

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Correspondence to Onur Ganiler.

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Ganiler, O., Oliver, A., Diez, Y. et al. A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies. Neuroradiology 56, 363–374 (2014). https://doi.org/10.1007/s00234-014-1343-1

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Keywords

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