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Reliability Guided Forward and Backward Patch-Based Method for Multi-atlas Segmentation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9467)

Abstract

Label fusion is an important step in multi-atlas based segmentation. It uses label propagation from multiple atlases to predict final label. However, most of the current label fusion methods consider each voxel equally and independently during the procedure of label fusion. In general, voxels which are misclassified are at the edge of ROIs, meanwhile the voxels labeled correctly with high reliability are far from the edge of ROIs. In light of this, we propose a novel framework for multi-atlas based image segmentation by using voxels of the target image with high reliability to guide the labeling procedure of other voxels with low reliability to afford more accurate label fusion. Specifically, we first measure the corresponding labeling reliability for each voxel based on traditional label fusion result, i.e., nonlocal mean weighted voting methods. In the second step, we use the voxels with high reliability to guide the label fusion process, at the same time we consider the location relationship of different voxels. We propagate not only labels from atlases, but also labels from the neighboring voxels with high reliability on the target. Meanwhile, an alternative method is supplied, we utilize the backward nonlocal mean patch-based method for reliability estimation. We compare our method with nonlocal mean patch-based method. In experiments, we apply all methods in the NIREP dataset to 32 regions of interest segmentation. Experimental results show our method can improve the performance of the nonlocal mean patch-based method.

Keywords

Patch-based Methods (PBM) Multi-atlas Segmentation Label Fusion Reliable Labeling Target 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.

Notes

Acknowledgments

We thank the reviewers for their helpful comments. This study was supported by the National Natural Science Foundation of China (61422204; 61473149); Jiangsu Natural Science Foundation for Young Scholar (BK20130034).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina

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