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Automatic annotation of anatomical structures can help simplify workflowduring interventions in numerous clinical applications but usually involves alarge amount of annotated data. The complexity of the labeling task, togetherwith the lack of representative data, slows down the development of robustsolutions. In this paper, we propose a solution requiring very few annotatedcases to label 3D pelvic arterial trees of patients with benign prostatichyperplasia. We take advantage of Large Deformation Diffeomorphic MetricMapping (LDDMM) to perform registration based on meaningful deformations fromwhich we build an atlas. Branch pairing is then computed from the atlas to newcases using optimal transport to ensure one-to-one correspondence during thelabeling process. To tackle topological variations in the tree, which usuallydegrades the performance of atlas-based techniques, we propose a simplebottom-up label assignment adapted to the pelvic anatomy. The proposed methodachieves 97.6\% labeling precision with only 5 cases for training, while incomparison learning-based methods only reach 82.2\% on such small trainingsets.

Database Annotation with few Examples: An Atlas-based Framework using Diffeomorphic Registration of 3D Trees