mlsanityData to 7 Oct 2026

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

arXiv 2501.10098 ↗First posted 17 Jan 2025cs.CV cs.AI cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 42 references that are in the map. First posted on arXiv 17 Jan 2025.

Builds on

22 of 42 references.

References · 22

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10 more
  1. 2023STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detectioncited by 41
  2. 2019An Attention-Guided Deep Regression Model for Landmark Detection in Cephalogramscited by 27
  3. 2020Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studiescited by 25
  4. 2021Subpixel Heatmap Regression for Facial Landmark Localizationcited by 14
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  10. 2021Point detection through multi-instance deep heatmap regression for sutures in endoscopycited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detectioncited by 3
  2. 2025Reliable uncertainty quantification for 2D/3D anatomical landmark localization using multi-output conformal predictioncited by 2
  3. 2026Chronological Contrastive Learning: Few-Shot Progression Assessment in Irreversible Diseasescited by 0