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Statistical shape modeling is an essential tool for the quantitative analysisof anatomical populations. Point distribution models (PDMs) represent theanatomical surface via a dense set of correspondences, an intuitive andeasy-to-use shape representation for subsequent applications. Thesecorrespondences are exhibited in two coordinate spaces: the local coordinatesdescribing the geometrical features of each individual anatomical surface andthe world coordinates representing the population-level statistical shapeinformation after removing global alignment differences across samples in thegiven cohort. We propose a deep-learning-based framework that simultaneouslylearns these two coordinate spaces directly from the volumetric images. Theproposed joint model serves a dual purpose; the world correspondences candirectly be used for shape analysis applications, circumventing the heavypre-processing and segmentation involved in traditional PDM models.Additionally, the local correspondences can be used for anatomy segmentation.We demonstrate the efficacy of this joint model for both shape modelingapplications on two datasets and its utility in inferring the anatomicalsurface.

Learning Population-level Shape Statistics and Anatomy Segmentation From Images: A Joint Deep Learning Model