Multi-atlas Segmentation without Registration: A Supervoxel-Based Approach

Abstract

Multi-atlas segmentation is a powerful segmentation technique. It has two components: label transfer that transfers segmentation labels from prelabeled atlases to a novel image and label fusion that combines the label transfer results. For reliable label transfer, most methods assume that the structure of interest to be segmented have localized spatial support across different subjects. Although the technique has been successful for many applications, the strong assumption also limits its applicability. For example, multi-atlas segmentation has not been applied for tumor segmentation because it is difficult to derive reliable label transfer for such applications due to the substantial variation in tumor locations. To address this limitation, we propose a label transfer technique for multi-atlas segmentation. Inspired by the Superparsing work [13], we approach this problem in two steps. Our method first oversegments images into homogeneous regions, called supervoxels. For a voxel in a novel image, to find its correspondence in atlases for label transfer, we first locate supervoxels in atlases that are most similar to the supervoxel the target voxel belongs to. Then, voxel-wise correspondence is found through searching for voxels that have most similar patches to the target voxel within the selected atlas supervoxels. We apply this technique for brain tumor segmentation and show promising results.

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