用点云检测骨骼关键点，跨物种通用且自动识别。
LmPT: Conditional Point Transformer for Anatomical Landmark Detection on 3D Point Clouds
- 基于条件点变换器，融合跨物种同源骨信息进行学习。
- 在人类与犬类股骨上实现高精度关键点定位，泛化能力强。
- 适合医学影像分析、动物模型研究等需要跨物种对比的场景。
准确识别解剖学关键点对多种医疗应用至关重要。传统人工标注耗时且存在观察者差异，基于规则的方法通常仅适用于特定几何结构或有限的关键点集。近年来，解剖表面可表示为由空间坐标构成的轻量点云。为此，我们提出一种名为LmPT（Landmark Point Transformer）的方法，用于在点云上自动检测解剖关键点，并可利用不同物种的同源骨骼支持转化研究。该模型引入条件机制，使系统能适应不同输入类型，实现跨物种学习。我们在人类及新标注的犬类股骨上评估该方法，验证其在跨物种场景下的泛化性与有效性。代码与犬类股骨数据集将公开于：https://github.com/Pierreoo/LandmarkPointTransformer。
原文摘要 · Abstract (English)
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.
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