 |
    |
Minimizing Joint Risk of Mislabeling for Iterative Patch-Based Label Fusion
Guorong Wu1, Qian Wang1, Shu Liao1, Daoqiang Zhang2, Feiping Nie3, and Dinggang Shen1
1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
2Department of Computer Science, Nanjing University of Aeronautics and Astronautics, China
3Department of Computer Science, University of Texas Arlington, USA
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.
LNCS 8151, p. 551 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013