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Minimizing Joint Risk of Mislabeling for Iterative Patch-Based Label Fusion

  • Guorong Wu
  • Qian Wang
  • Shu Liao
  • Daoqiang Zhang
  • Feiping Nie
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8151)

Abstract

Automated labeling of anatomical structures in medical images is very important in many neuroscience studies. Recently, patch-based labeling in the non-local manner has been widely investigated to alleviate the possible misalignment when registering atlases to the target image. However, the weights used for label fusion from the registered atlases in conventional methods are generally computed independently and thus lack the capability of preventing the ambiguous atlas patches from contributing to the label fusion. More critically, these weights are often calculated based only on the simple patch similarity, thus not necessarily providing optimal solution for label fusion. To address these issues, we present a novel patch-based label fusion method in multi-atlas scenario, for the goal of labeling each voxel in the target image by the best representative atlas patches that also have the lowest joint risk of mislabeling. Specifically, sparse coding is used to select a small number of atlas patches which best represent the underlying patch at each point of the target image, thus minimizing the chance of including the misleading atlas patches for labeling. Furthermore, we examine the joint risk of any pair of atlas patches in making similar labeling error, by analyzing the correlation of their morphological error patterns and also the labeling consensus among atlases. This joint risk will be further recursively updated based on the latest labeling results to correct the possible labeling errors. To demonstrate the performance of our proposed method, we have evaluated it on both whole brain parcellation and hippocampus segmentation, and achieved promising labeling results, compared with the state-of- the-art methods.

Keywords

Target Image Sparse Code Sparsity Constraint Label Result Target Patch 
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-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guorong Wu
    • 1
  • Qian Wang
    • 1
  • Shu Liao
    • 1
  • Daoqiang Zhang
    • 2
  • Feiping Nie
    • 3
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA
  2. 2.Department of Computer ScienceNanjing University of Aeronautics and AstronauticsChina
  3. 3.Department of Computer ScienceUniversity of Texas ArlingtonUSA

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