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

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.