专为医学影像设计的解剖标志点定位工具包，提升精度与开发效率。
landmarker: a Toolkit for Anatomical Landmark Localization in 2D/3D Images
- 基于PyTorch构建，支持静态与自适应热图回归方法
- 可处理多种图像格式与预处理流程，提升定位准确性
- 模块化设计，适合定制化医学数据集研究与开发
二维和三维医学影像中的解剖标志点定位是医学影像领域的关键任务。尽管已有诸多通用计算机视觉工具可用于姿态估计等任务，但它们缺乏针对医学领域解剖标志点定位所需的专用功能与模块化设计。为此，我们提出landmarker，一个基于PyTorch的Python工具包，提供全面、灵活的开发与评估环境，支持静态与自适应热图回归等多种方法。该工具包可提升标志点识别精度，简化研发流程，并兼容多种图像格式与预处理管道。其模块化架构允许用户根据特定数据集与应用进行定制与扩展，加速医学影像领域的创新。landmarker填补了现有通用姿态估计工具在精度与定制性方面未能满足的空白。
原文摘要 · Abstract (English)
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.
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