Format

Send to

Choose Destination
IEEE Trans Pattern Anal Mach Intell. 2013 Mar;35(3):611-23. doi: 10.1109/TPAMI.2012.143. Epub 2012 Jun 26.

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.

PMID:
22732662
PMCID:
PMC3864549
DOI:
10.1109/TPAMI.2012.143
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for IEEE Engineering in Medicine and Biology Society Icon for PubMed Central
Loading ...
Support Center