Multi-Atlas Segmentation with Joint Label Fusion

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.

Fig. 1

Optimal label fusion parameter selection for LWGaussian (left), LWInverse (middle) and LWJoint (right) using leave-one-out cross-validation. The upper figures plot the average number of mislabeled voxels against the local searching radius r_s and the appearance window radius r. The weighting function parameters σ, β are held fixed in these figures at its optimal value for the three methods, respectively. The lower figures plot the average number of mislabeled voxels against the local searching radius r_s and the weighting function parameter, σ and β, respectively. The appearance window radius r is held fixed in this figure at its optimal value.

Fig. 2

Sagittal views of a segmentation produced by LWGaussian and our method. Red: reference segmentation; Blue: automatic segmentation; Green: overlap between manual and automatic segmentation.

Fig. 3

Optimal label fusion parameter selection for LWGaussian (left), LWInverse (middle) and LWJoint (right) using leave-one-out cross-validation. The upper figures plot the average number of mislabeled voxels against the local searching radius r_s and the appearance window radius r. The weighting function parameters σ, β are held fixed in these figures at its optimal value for the three methods, respectively. The lower figures plot the average number of mislabeled voxels against the local searching radius r_s and the weighting function parameter, σ and β, respectively. The appearance window radius r is held fixed in this figure at its optimal value.

Fig. 4

Coronal views of some subfield segmentation results produced by MV, LWGaussian and our method. All results are produced with local searching using the optimal parameter for each method. Label description: red - CA1; green - CA2; yellow - CA3; blue - DG; light brown - miscellaneous label; brown - SUB; cyan - ERC; pink - PHG.