  Brain midline shift (MLS) is one of the most critical factors to be
considered for clinical diagnosis and treatment decision-making for
intracranial hemorrhage. Existing computational methods on MLS quantification
not only require intensive labeling in millimeter-level measurement but also
suffer from poor performance due to their dependence on specific landmarks or
simplified anatomical assumptions. In this paper, we propose a novel
semi-supervised framework to accurately measure the scale of MLS from head CT
scans. We formulate the MLS measurement task as a deformation estimation
problem and solve it using a few MLS slices with sparse labels. Meanwhile, with
the help of diffusion models, we are able to use a great number of unlabeled
MLS data and 2793 non-MLS cases for representation learning and regularization.
The extracted representation reflects how the image is different from a non-MLS
image and regularization serves an important role in the sparse-to-dense
refinement of the deformation field. Our experiment on a real clinical brain
hemorrhage dataset has achieved state-of-the-art performance and can generate
interpretable deformation fields.
