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Multi-atlas and Multi-modal Hippocampus Segmentation for Infant MR Brain Images by Propagating Anatomical Labels on Hypergraph

  • Pei Dong
  • Yanrong Guo
  • Dinggang Shen
  • Guorong WuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9467)

Abstract

Accurate segmentation of hippocampus from infant magnetic resonance (MR) images is very important in the study of early brain development and neurological disorder. Recently, multi-atlas patch-based label fusion methods have shown a great success in segmenting anatomical structures from medical images. However, the dramatic appearance change from birth to 1-year-old and the poor image contrast make the existing label fusion methods less competitive to handle infant brain images. To alleviate these difficulties, we propose a novel multi-atlas and multi-modal label fusion method, which can unanimously label for all voxels by propagating the anatomical labels on a hypergraph. Specifically, we consider not only all voxels within the target image but also voxels across the atlas images as the vertexes in the hypergraph. Each hyperedge encodes a high-order correlation, among a set of vertexes, in different perspectives which incorporate (1) feature affinity within the multi-modal feature space, (2) spatial coherence within target image, and (3) population heuristics from multiple atlases. In addition, our label fusion method further allows those reliable voxels to supervise the label estimation on other difficult-to-label voxels, based on the established hyperedges, until all the target image voxels reach the unanimous labeling result. We evaluate our proposed label fusion method in segmenting hippocampus from T1 and T2 weighted MR images acquired from at 2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old. Our segmentation results achieves improvement of labeling accuracy over the conventional state-of-the-art label fusion methods, which shows a great potential to facilitate the early infant brain studies.

Keywords

Target Image Label Propagation Deformable Image Registration Anatomical Label Atlas Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pei Dong
    • 1
  • Yanrong Guo
    • 1
  • Dinggang Shen
    • 1
  • Guorong Wu
    • 1
    Email author
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

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