ArXiv Preprint
Vertebral fractures are a consequence of osteoporosis, with significant
health implications for affected patients. Unfortunately, grading their
severity using CT exams is hard and subjective, motivating automated grading
methods. However, current approaches are hindered by imbalance and scarcity of
data and a lack of interpretability. To address these challenges, this paper
proposes a novel approach that leverages unlabelled data to train a generative
Diffusion Autoencoder (DAE) model as an unsupervised feature extractor. We
model fracture grading as a continuous regression, which is more reflective of
the smooth progression of fractures. Specifically, we use a binary, supervised
fracture classifier to construct a hyperplane in the DAE's latent space. We
then regress the severity of the fracture as a function of the distance to this
hyperplane, calibrating the results to the Genant scale. Importantly, the
generative nature of our method allows us to visualize different grades of a
given vertebra, providing interpretability and insight into the features that
contribute to automated grading.
Matthias Keicher, Matan Atad, David Schinz, Alexandra S. Gersing, Sarah C. Foreman, Sophia S. Goller, Juergen Weissinger, Jon Rischewski, Anna-Sophia Dietrich, Benedikt Wiestler, Jan S. Kirschke, Nassir Navab
2023-03-21
