landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D Images

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2025-05-01 DOI:10.1016/j.softx.2025.102165

Jef Jonkers , Luc Duchateau , Glenn Van Wallendael , Sofie Van Hoecke

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引用次数: 0

Abstract

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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landmarker：用于二维/三维图像解剖地标定位的工具包

二维/三维图像解剖地标定位是医学成像中的一项关键任务。尽管在经典计算机视觉任务中存在许多通用的地标定位工具，如姿态估计，但它们缺乏医学领域解剖地标定位应用所需的专门功能和模块化。因此，我们引入了一个基于PyTorch构建的Python包landmarker。该软件包为开发和评估地标定位算法提供了一个全面、灵活的工具包，支持一系列方法，包括静态和自适应热图回归。Landmarker提高了地标识别的准确性，简化了研发过程，并支持各种图像格式和预处理管道。其模块化设计允许用户定制和扩展特定数据集和应用程序的工具包，加速医学成像的创新。Landmarker解决了现有通用姿态估计工具无法充分满足的地标定位任务中精度和定制的关键需求。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.