Joint segmentation via patient-specific latent anatomy model
Résumé
We present a generative approach for joint 3D segmentation of patient-speciØc MR scans across diÆerent modalities or time points. The latent anatomy, in the form of spatial parameters, is inferred si- multaneously 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