LmPT: Conditional Point Transformer for Anatomical Landmark Detection on 3D Point Clouds
This work addresses the need for efficient and consistent landmark detection in medical applications, offering a method that reduces manual effort and variability, though it is incremental as it builds on existing point cloud and transformer techniques.
The paper tackles the problem of automatic anatomical landmark detection on 3D point clouds, proposing the Landmark Point Transformer (LmPT) method, which achieves generalization across species by leveraging cross-species learning on human and dog femurs.
Accurate identification of anatomical landmarks is crucial for various medical applications. Traditional manual landmarking is time-consuming and prone to inter-observer variability, while rule-based methods are often tailored to specific geometries or limited sets of landmarks. In recent years, anatomical surfaces have been effectively represented as point clouds, which are lightweight structures composed of spatial coordinates. Following this strategy and to overcome the limitations of existing landmarking techniques, we propose Landmark Point Transformer (LmPT), a method for automatic anatomical landmark detection on point clouds that can leverage homologous bones from different species for translational research. The LmPT model incorporates a conditioning mechanism that enables adaptability to different input types to conduct cross-species learning. We focus the evaluation of our approach on femoral landmarking using both human and newly annotated dog femurs, demonstrating its generalization and effectiveness across species. The code and dog femur dataset will be publicly available at: https://github.com/Pierreoo/LandmarkPointTransformer.