Image2SSM: Reimagining Statistical Shape Models from Images with Radial Basis Functions.

ABSTRACT: Statistical shape modeling (SSM) is an essential tool for analyzing variations in anatomical morphology. In a typical SSM pipeline, 3D anatomical images, gone through segmentation and rigid registration, are represented using lower-dimensional shape features, on which statistical analysis can be performed. Various methods for constructing compact shape representations have been proposed, but they involve laborious and costly steps. We propose Image2SSM, a novel deep-learning-based approach for SSM that leverages image-segmentation pairs to learn a radial-basis-function (RBF)-based representation of shapes directly from images. This RBF-based shape representation offers a rich self-supervised signal for the network to estimate a continuous, yet compact representation of the underlying surfa

SUBMITTER: Xu H 

PROVIDER: S-EPMC11555643 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature