Image2SSM: Reimagining Statistical Shape Models from Images with Radial Basis Functions
This work addresses the scalability problem for researchers and practitioners in medical imaging and anatomy analysis by automating SSM, though it appears incremental as it builds on existing deep learning and RBF techniques.
The authors tackled the laborious and costly steps in statistical shape modeling (SSM) by proposing Image2SSM, a deep-learning approach that learns radial-basis-function-based shape representations directly from images, enabling inference from unsegmented images and showing efficacy compared to state-of-the-art methods in experiments on synthetic and real datasets.
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 surface that can adapt to complex geometries in a data-driven manner. Image2SSM can characterize populations of biological structures of interest by constructing statistical landmark-based shape models of ensembles of anatomical shapes while requiring minimal parameter tuning and no user assistance. Once trained, Image2SSM can be used to infer low-dimensional shape representations from new unsegmented images, paving the way toward scalable approaches for SSM, especially when dealing with large cohorts. Experiments on synthetic and real datasets show the efficacy of the proposed method compared to the state-of-art correspondence-based method for SSM.