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Supervoxel-Based Hierarchical Markov Random Field Framework for Multi-atlas Segmentation

  • Ning YuEmail author
  • Hongzhi Wang
  • Paul A. Yushkevich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9993)

Abstract

Multi-atlas segmentation serves as an important technique for quantitative analysis of medical images. In many applications, top performing techniques rely on computationally expensive deformable registration to transfer labels from atlas images to the target image. We propose a more computationally efficient label transfer strategy that uses supervoxel matching regularized by Markov random field (MRF), followed by regional voxel-wise joint label fusion and a second MRF. We evaluate this hierarchical MRF framework for multi-label diencephalon segmentation from the MICCAI 2013 SATA Challenge. Our segmentation results are comparable to the top-tier one obtained by deformable registration, but with much lower computational complexity.

Keywords

Segmentation Atlas Supervoxel MRF 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of VirginiaCharlottesvilleUSA
  2. 2.IBM Almaden Research CenterSan JoseUSA
  3. 3.Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA

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