Anatomical landmark localization in 2D/3D images is a critical task in medical imaging. Although many general-purpose tools exist for landmark localization in classical computer vision tasks, such as pose estimation, they lack the specialized features and modularity necessary for anatomical landmark localization applications in the medical domain. Therefore, we introduce landmarker, a Python package built on PyTorch. The package provides a comprehensive, flexible toolkit for developing and evaluating landmark localization algorithms, supporting a range of methodologies, including static and adaptive heatmap regression. landmarker enhances the accuracy of landmark identification, streamlines research and development processes, and supports various image formats and preprocessing pipelines. Its modular design allows users to customize and extend the toolkit for specific datasets and applications, accelerating innovation in medical imaging. landmarker addresses a critical need for precision and customization in landmark localization tasks not adequately met by existing general-purpose pose estimation tools.

本研究针对目前医疗影像中解剖标志定位工具缺乏专业性的实际问题，提出了一款名为landmarker的Python工具包，基于PyTorch构建，支持多种方法，包括静态和自适应热图回归。该工具包显著提升了解剖标志识别的准确性，并通过模块化设计简化了研发流程，促进了医疗影像领域的创新。

landmarker：用于2D/3D图像解剖标志定位的工具包