  Statistical shape modeling (SSM) characterizes anatomical variations in a
population of shapes generated from medical images. SSM requires consistent
shape representation across samples in shape cohort. Establishing this
representation entails a processing pipeline that includes anatomy
segmentation, re-sampling, registration, and non-linear optimization. These
shape representations are then used to extract low-dimensional shape
descriptors that facilitate subsequent analyses in different applications.
However, the current process of obtaining these shape descriptors from imaging
data relies on human and computational resources, requiring domain expertise
for segmenting anatomies of interest. Moreover, this same taxing pipeline needs
to be repeated to infer shape descriptors for new image data using a
pre-trained/existing shape model. Here, we propose DeepSSM, a deep
learning-based framework for learning the functional mapping from images to
low-dimensional shape descriptors and their associated shape representations,
thereby inferring statistical representation of anatomy directly from 3D
images. Once trained using an existing shape model, DeepSSM circumvents the
heavy and manual pre-processing and segmentation and significantly improves the
computational time, making it a viable solution for fully end-to-end SSM
applications. In addition, we introduce a model-based data-augmentation
strategy to address data scarcity. Finally, this paper presents and analyzes
two different architectural variants of DeepSSM with different loss functions
using three medical datasets and their downstream clinical application.
Experiments showcase that DeepSSM performs comparably or better to the
state-of-the-art SSM both quantitatively and on application-driven downstream
tasks. Therefore, DeepSSM aims to provide a comprehensive blueprint for deep
learning-based image-to-shape models.
