LmPT: Conditional Point Transformer for Anatomical Landmark Detection on 3D Point Clouds

2026-02-02

AI Summary

This work addresses the limitations of conventional methods in 3D anatomical landmark detection—namely, low efficiency, poor generalization, and inadequate cross-species adaptability—by proposing LmPT, a unified framework based on a conditional point cloud Transformer. By incorporating a conditioning mechanism, LmPT flexibly accommodates diverse input types and, for the first time, enables joint modeling and transfer learning of homologous skeletal landmarks across species, such as humans and dogs. Experimental results on a newly curated dataset of human femurs and a newly annotated dataset of canine femurs demonstrate that LmPT achieves high accuracy while exhibiting strong cross-species generalization capabilities. The code and datasets are publicly released to facilitate further research.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

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.

Problem

• anatomical landmark detection
• 3D point clouds
• cross-species learning
• femoral landmarking
• automatic landmarking

Innovation

• Conditional Point Transformer
• Anatomical Landmark Detection
• 3D Point Clouds
• Cross-species Learning
• Femoral Landmarking