landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D Images
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | SoftwareX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711025001323 |
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| author | Jef Jonkers Luc Duchateau Glenn Van Wallendael Sofie Van Hoecke |
| author_facet | Jef Jonkers Luc Duchateau Glenn Van Wallendael Sofie Van Hoecke |
| author_sort | Jef Jonkers |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-894eba1b9eaa40bb8bc746094c4503c3 |
| institution | OA Journals |
| issn | 2352-7110 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj-art-894eba1b9eaa40bb8bc746094c4503c32025-08-20T02:30:18ZengElsevierSoftwareX2352-71102025-05-013010216510.1016/j.softx.2025.102165landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D ImagesJef Jonkers0Luc Duchateau1Glenn Van Wallendael2Sofie Van Hoecke3IDLab, Department of Electronics and Information Systems, Ghent University, Belgium; Corresponding author.Biometrics Research Group, Department of Morphology, Imaging, Orthopedics, Rehabilitation and Nutrition, Ghent University, BelgiumIDLab, Department of Electronics and Information Systems, Ghent University - imec, BelgiumIDLab, Department of Electronics and Information Systems, Ghent University - imec, BelgiumAnatomical 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.http://www.sciencedirect.com/science/article/pii/S2352711025001323Landmark localizationKeypoint detectionMedical image analysisPythonPyTorch |
| spellingShingle | Jef Jonkers Luc Duchateau Glenn Van Wallendael Sofie Van Hoecke landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D Images SoftwareX Landmark localization Keypoint detection Medical image analysis Python PyTorch |
| title | landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D Images |
| title_full | landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D Images |
| title_fullStr | landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D Images |
| title_full_unstemmed | landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D Images |
| title_short | landmarker: A Toolkit for Anatomical Landmark Localization in 2D/3D Images |
| title_sort | landmarker a toolkit for anatomical landmark localization in 2d 3d images |
| topic | Landmark localization Keypoint detection Medical image analysis Python PyTorch |
| url | http://www.sciencedirect.com/science/article/pii/S2352711025001323 |
| work_keys_str_mv | AT jefjonkers landmarkeratoolkitforanatomicallandmarklocalizationin2d3dimages AT lucduchateau landmarkeratoolkitforanatomicallandmarklocalizationin2d3dimages AT glennvanwallendael landmarkeratoolkitforanatomicallandmarklocalizationin2d3dimages AT sofievanhoecke landmarkeratoolkitforanatomicallandmarklocalizationin2d3dimages |