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

TL;DR

This paper introduces an atlas-based framework using diffeomorphic registration to efficiently annotate 3D pelvic arterial trees with minimal training data, significantly outperforming learning-based methods on small datasets.

Contribution

The novel approach combines LDDMM registration, atlas construction, and optimal transport for accurate labeling with very few annotated examples.

Findings

Achieves 97.6% labeling precision with only 5 training cases.

Outperforms learning-based methods which reach 82.2% on small datasets.

Effectively handles topological variations in arterial trees.

Abstract

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.