  Correspondence-based shape models are key to various medical imaging
applications that rely on a statistical analysis of anatomies. Such shape
models are expected to represent consistent anatomical features across the
population for population-specific shape statistics. Early approaches for
correspondence placement rely on nearest neighbor search for simpler anatomies.
Coordinate transformations for shape correspondence hold promise to address the
increasing anatomical complexities. Nonetheless, due to the inherent
shape-level geometric complexity and population-level shape variation, the
coordinate-wise correspondence often does not translate to the anatomical
correspondence. An alternative, group-wise approach for correspondence
placement explicitly models the trade-off between geometric description and the
population's statistical compactness. However, these models achieve limited
success in resolving nonlinear shape correspondence. Recent works have
addressed this limitation by adopting an application-specific notion of
correspondence through lifting positional data to a higher dimensional feature
space. However, they heavily rely on manual expertise to create domain-specific
features and consistent landmarks. This paper proposes an automated feature
learning approach, using deep convolutional neural networks to extract
correspondence-friendly features from shape ensembles. Further, an unsupervised
domain adaptation scheme is introduced to augment the pretrained geometric
features with new anatomies. Results on anatomical datasets of human scapula,
femur, and pelvis bones demonstrate that features learned in supervised fashion
show improved performance for correspondence estimation compared to the manual
features. Further, unsupervised learning is demonstrated to learn complex
anatomy features using the supervised domain adaptation from features learned
on simpler anatomy.
