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Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion

  • Mengjin DongEmail author
  • Ipek Oguz
  • Nagesh Subbana
  • Peter Calabresi
  • Russell T. Shinohara
  • Paul Yushkevich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10530)

Abstract

This paper adapts the joint label fusion (JLF) multi-atlas image segmentation algorithm to the problem of multiple sclerosis (MS) lesion segmentation in multi-modal MRI. Conventionally, JLF requires a set of atlas images to be co-registered to the target image using deformable registration. However, given the variable spatial distribution of lesions in the brain, whole-brain deformable registration is unlikely to line up lesions between atlases and the target image. As a solution, we propose to first pre-segment the target image using an intensity regression based technique, yielding a set of “candidate” lesions. Each “candidate” lesion is then matched to a set of similar lesions in the atlas based on location and size; and deformable registration and JLF are applied at the level of the “candidate” lesion. The approach is evaluated on a dataset of 74 subjects with MS and shown to improve Dice similarity coefficient with reference manual segmentation by 12% over intensity regression technique.

Notes

Acknowledgments

This work was supported, in part, by the NIH grants NIBIB R01EB017255, NINDS R01NS082347, NINDS R01NS085211, NINDS R21NS093349, NINDS R01NS094456.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mengjin Dong
    • 1
    Email author
  • Ipek Oguz
    • 1
  • Nagesh Subbana
    • 1
  • Peter Calabresi
    • 2
  • Russell T. Shinohara
    • 3
  • Paul Yushkevich
    • 1
  1. 1.Penn Image Computing and Science LabUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.The Johns Hopkins Calabresi LabJohns Hopkins UniversityBaltimoreUSA
  3. 3.Center for Clinical Epidemiology and BiostatisticsUniversity of PennsylvaniaPhiladelphiaUSA

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