Multi-Atlas Segmentation with Joint Label Fusion

TLDR

Multi‑atlas segmentation labels biomedical images by registering multiple expert atlases to a target and fusing their deformations, but current weighted voting methods compute atlas weights independently, ignoring correlated labeling errors. We propose a joint label fusion approach that models pairwise atlas dependencies as joint error probabilities to minimize the expected labeling error. The joint error probability is approximated from intensity similarity between atlas pairs and the target in each voxel’s neighborhood, and the method is validated on hippocampus and hippocampus subfield segmentation in MR images. The proposed method achieves consistent, significant improvements over independent‑weight strategies, confirming that spatially varying weights derived from atlas‑target similarity outperform prior approaches.

Abstract

Multi-atlas segmentation is an effective approach for automatically labeling objects of interest in biomedical images. In this approach, multiple expert-segmented example images, called atlases, are registered to a target image, and deformed atlas segmentations are combined using label fusion. Among the proposed label fusion strategies, weighted voting with spatially varying weight distributions derived from atlas-target intensity similarity have been particularly successful. However, one limitation of these strategies is that the weights are computed independently for each atlas, without taking into account the fact that different atlases may produce similar label errors. To address this limitation, we propose a new solution for the label fusion problem in which weighted voting is formulated in terms of minimizing the total expectation of labeling error and in which pairwise dependency between atlases is explicitly modeled as the joint probability of two atlases making a segmentation error at a voxel. This probability is approximated using intensity similarity between a pair of atlases and the target image in the neighborhood of each voxel. We validate our method in two medical image segmentation problems: hippocampus segmentation and hippocampus subfield segmentation in magnetic resonance (MR) images. For both problems, we show consistent and significant improvement over label fusion strategies that assign atlas weights independently.