Learning Population-level Shape Statistics and Anatomy Segmentation From Images: A Joint Deep Learning Model
This work addresses the need for efficient shape analysis and segmentation in medical imaging, offering a novel approach that reduces preprocessing steps, though it appears incremental in combining existing concepts.
The authors tackled the problem of statistical shape modeling and anatomy segmentation by proposing a joint deep learning model that learns both local and world coordinate spaces directly from volumetric images, achieving dual-purpose functionality for shape analysis and segmentation without heavy preprocessing.
Statistical shape modeling is an essential tool for the quantitative analysis of anatomical populations. Point distribution models (PDMs) represent the anatomical surface via a dense set of correspondences, an intuitive and easy-to-use shape representation for subsequent applications. These correspondences are exhibited in two coordinate spaces: the local coordinates describing the geometrical features of each individual anatomical surface and the world coordinates representing the population-level statistical shape information after removing global alignment differences across samples in the given cohort. We propose a deep-learning-based framework that simultaneously learns these two coordinate spaces directly from the volumetric images. The proposed joint model serves a dual purpose; the world correspondences can directly be used for shape analysis applications, circumventing the heavy pre-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 modeling applications on two datasets and its utility in inferring the anatomical surface.