  Automatic annotation of anatomical structures can help simplify workflow
during interventions in numerous clinical applications but usually involves a
large amount of annotated data. The complexity of the labeling task, together
with the lack of representative data, slows down the development of robust
solutions. In this paper, we propose a solution requiring very few annotated
cases to label 3D pelvic arterial trees of patients with benign prostatic
hyperplasia. We take advantage of Large Deformation Diffeomorphic Metric
Mapping (LDDMM) to perform registration based on meaningful deformations from
which we build an atlas. Branch pairing is then computed from the atlas to new
cases using optimal transport to ensure one-to-one correspondence during the
labeling process. To tackle topological variations in the tree, which usually
degrades the performance of atlas-based techniques, we propose a simple
bottom-up label assignment adapted to the pelvic anatomy. The proposed method
achieves 97.6\% labeling precision with only 5 cases for training, while in
comparison learning-based methods only reach 82.2\% on such small training
sets.
