Projects:LatentAtlasSegmentation

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Joint Segmentation of Image Ensembles via Latent Atlases

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas. Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method by segmenting 50 brain MR volumes. Segmentation accuracy for cortical and subcortical structures approaches the quality of state-of-the-art atlas-based segmentation results, suggesting that the latent atlas method is a reasonable alternative when existing atlases are not compatible with the data to be processed.

Results

We test the proposed approach on 50 MR brain scans. Some of the subjects in this set are diagnosed with the first episode schizophrenia or affective disorder. The MR images (T1, 256X256X128 volume, 0.9375 X 0.9375 X 1.5 mm voxel size) were acquired by a 1.5-T General Electric Scanner. In addition to the MR volumes, manual segmentations of three structures (superior temporal gyrus, amygdala, and hippocampus) in each hemisphere were provided for each of the 50 individuals and used to evaluate the quality of the automatic segmentation results. MR images are preprocessed by skull stripping. The volumes were aligned using B-spline registration.

Three cross-sections of 3D segmentations of Hippocampus, Amygdala and Superior Temporal Gyrus in the left and the right hemispheres. Automatic segmentation is shown in red. Manual segmentation is shown in blue. Fourth column: Coronal views of the resulting atlases for each pair of structures.