Joint segmentation via patient-specific latent anatomy model

We present a generative approach for joint 3D segmentation of patient-specific MR scans across different modalities or time points. The latent anatomy, in the form of spatial parameters, is inferred simultaneously with the evolution of the segmentations. The individual segmentation of each scan supports the segmentation of the group by sharing common information. The joint segmentation problem is solved via a statistically driven level-set framework. We illustrate the method on an example application of multimodal and longitudinal brain tumor segmentation, reporting promising segmentation results.