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Multi-atlas Parcellation in the Presence of Lesions: Application to Multiple Sclerosis

  • Sandra González-Villà
  • Yuankai Huo
  • Arnau Oliver
  • Xavier Lladó
  • Bennett A. Landman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11075)

Abstract

Intensity-based multi-atlas strategies have shown leading performance in segmenting healthy subjects, but when lesions are present, the abnormal lesion intensities affect the fusion result. Here, we propose a reformulated statistical fusion approach for multi-atlas segmentation that is applicable to both healthy and injured brains. This method avoids the interference of lesion intensities on the segmentation by incorporating two a priori masks to the Non-Local STAPLE statistical framework. First, we extend the theory to include a lesion mask, which improves the voxel correspondence between the target and the atlases. Second, we extend the theory to include a known label mask, that forces the label decision in case it is beforehand known and enables seamless integration of manual edits. We evaluate our method with simulated and MS patient images and compare our results with those of other state-of-the-art multi-atlas strategies: Majority vote, Non-local STAPLE, Non-local Spatial STAPLE and Joint Label Fusion. Quantitative and qualitative results demonstrate the improvement in the lesion areas.

Keywords

Brain parcellation Segmentation Multiple sclerosis 

Notes

Acknowledgements

The authors would like to thank Jose Bernal for helpful discussion. Sandra González-Villà holds a UdG-BRGR2015 grant. This work has been supported by “La Fundació la Marató de TV3”, by Retos de Investigación TIN2014-55710-R and TIN2015-73563-JIN, by DPI2017-86696-R and by UdG mobility grant MOB17. This research was supported by NSF CAREER 1452485, NIH R01-EB017230, and the National Center for Advancing Translational Sciences, Grant 2 UL1 TR000445-06. We appreciate the NIH S10 Shared Instrumentation Grant 1S10OD020154-01 (Smith), Vanderbilt IDEAS grant (Holly-Bockelmann, Walker, Meliler, Palmeri, Weller), and ACCRE’s Big Data TIPs grant from Vanderbilt University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, National Science Foundation, or other sponsors.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sandra González-Villà
    • 1
    • 2
  • Yuankai Huo
    • 2
  • Arnau Oliver
    • 1
  • Xavier Lladó
    • 1
  • Bennett A. Landman
    • 2
  1. 1.Institute of Computer Vision and RoboticsUniversity of GironaGironaSpain
  2. 2.Electrical EngineeringVanderbilt UniversityNashvilleUSA

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