MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy
  Statistical Shape Modeling Directly From Images
Janmesh Ukey Tushar Kataria Shireen Y. Elhabian
Statistical Shape Modeling (SSM) is an effective method for quantitatively
analyzing anatomical variations within populations. However, its utility is
limited by the need for manual segmentations of anatomies, a task that relies
on the scarce expertise of medical professionals. Recent advances in deep
learning have provided a promising approach that automatically generates
statistical representations from unsegmented images. Once trained, these deep
learning-based models eliminate the need for manual segmentation for new
subjects. Nonetheless, most current methods still require manual pre-alignment
of image volumes and specifying a bounding box around the target anatomy prior
for inference, resulting in a partially manual inference process. Recent
approaches facilitate anatomy localization but only estimate statistical
representations at the population level. However, they cannot delineate anatomy
directly in images and are limited to modeling a single anatomy. Here, we
introduce MASSM, a novel end-to-end deep learning framework that simultaneously
localizes multiple anatomies in an image, estimates population-level
statistical representations, and delineates each anatomy. Our findings
emphasize the crucial role of local correspondences, showcasing their
indispensability in providing superior shape information for medical imaging
tasks.