  Statistical Shape Models (SSMs) excel at identifying population level
anatomical variations, which is at the core of various clinical and biomedical
applications, including morphology-based diagnostics and surgical planning.
However, the effectiveness of SSM is often constrained by the necessity for
expert-driven manual segmentation, a process that is both time-intensive and
expensive, thereby restricting their broader application and utility. Recent
deep learning approaches enable the direct estimation of Statistical Shape
Models (SSMs) from unsegmented images. While these models can predict SSMs
without segmentation during deployment, they do not address the challenge of
acquiring the manual annotations needed for training, particularly in
resource-limited settings. Semi-supervised and foundation models for anatomy
segmentation can mitigate the annotation burden. Yet, despite the abundance of
available approaches, there are no established guidelines to inform end-users
on their effectiveness for the downstream task of constructing SSMs. In this
study, we systematically evaluate the potential of weakly supervised methods as
viable alternatives to manual segmentation's for building SSMs. We establish a
new performance benchmark by employing various semi-supervised and foundational
model methods for anatomy segmentation under low annotation settings, utilizing
the predicted segmentation's for the task of SSM. We compare the modes of shape
variation and use quantitative metrics to compare against a shape model derived
from a manually annotated dataset. Our results indicate that some methods
produce noisy segmentation, which is very unfavorable for SSM tasks, while
others can capture the correct modes of variations in the population cohort
with 60-80\% reduction in required manual annotation.
