Optimized PatchMatch for Near Real Time and Accurate Label Fusion

  • Vinh-Thong Ta
  • Rémi Giraud
  • D. Louis Collins
  • Pierrick Coupé
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)


Automatic segmentation methods are important tools for quantitative analysis of magnetic resonance images. Recently, patch- based label fusion approaches demonstrated state-of-the-art segmentation accuracy. In this paper, we introduce a new patch-based method using the PatchMatch algorithm to perform segmentation of anatomical structures. Based on an Optimized PAtchMatch Label fusion (OPAL) strategy, the proposed method provides competitive segmentation accuracy in near real time. During our validation on hippocampus segmentation of 80 healthy subjects, OPAL was compared to several state-of-the-art methods. Results show that OPAL obtained the highest median Dice coefficient (89.3%) in less than 1 sec per subject. These results highlight the excellent performance of OPAL in terms of computation time and segmentation accuracy compared to recently published methods.


PatchMatch Patch-based Segmentation Hippocampus 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Vinh-Thong Ta
    • 1
    • 2
    • 3
  • Rémi Giraud
    • 1
    • 2
    • 3
  • D. Louis Collins
    • 4
  • Pierrick Coupé
    • 1
    • 2
  1. 1.LaBRI, UMR 5800, PICTURAUniv. BordeauxTalenceFrance
  2. 2.CNRS, LaBRI, UMR 5800, PICTURATalenceFrance
  3. 3.IPB, LaBRI, UMR 5800, PICTURAPessacFrance
  4. 4.McConnell Brain Imaging Centre, Montreal Neurological InstituteMcGill UniversityMontrealCanada

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