Database Annotation with few Examples: An Atlas-based Framework using Diffeomorphic Registration of 3D Trees
This addresses the challenge of anatomical structure labeling in clinical applications where data is scarce, offering a practical solution for interventions in patients with benign prostatic hyperplasia.
The paper tackles the problem of automatic annotation of 3D pelvic arterial trees with limited annotated data, achieving 97.6% labeling precision using only 5 training cases, compared to 82.2% for learning-based methods.
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.