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[2507.11551] Landmark Detection for Medical Images using a General-purpose Segmentation Model
Summary
The paper presents a novel approach to anatomical landmark detection in medical images by combining YOLO and SAM models, enhancing segmentation accuracy for orthopaedic diagnostics.
Why It Matters
Accurate landmark detection in medical imaging is crucial for effective diagnosis and treatment planning in orthopaedics. This research addresses limitations in existing models, potentially improving diagnostic outcomes and streamlining workflows in medical imaging.
Key Takeaways
- Combining YOLO and SAM models improves landmark detection accuracy.
- The hybrid model can segment complex structures in orthopaedic radiographs.
- Existing models like SAM and MedSAM lack the precision needed for specific anatomical landmarks.
Electrical Engineering and Systems Science > Image and Video Processing arXiv:2507.11551 (eess) [Submitted on 13 Jul 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:Landmark Detection for Medical Images using a General-purpose Segmentation Model Authors:Ekaterina Stansfield, Jennifer A. Mitterer, Abdulrahman Altahhan View a PDF of the paper titled Landmark Detection for Medical Images using a General-purpose Segmentation Model, by Ekaterina Stansfield and 2 other authors View PDF HTML (experimental) Abstract:Radiographic images are a cornerstone of medical diagnostics in orthopaedics, with anatomical landmark detection serving as a crucial intermediate step for information extraction. General-purpose foundational segmentation models, such as SAM (Segment Anything Model), do not support landmark segmentation out of the box and require prompts to function. However, in medical imaging, the prompts for landmarks are highly specific. Since SAM has not been trained to recognize such landmarks, it cannot generate accurate landmark segmentations for diagnostic purposes. Even MedSAM, a medically adapted variant of SAM, has been trained to identify larger anatomical structures, such as organs and their parts, and lacks the fine-grained precision required for orthopaedic pelvic landmarks. To address this limitation, we propose leveraging another general-purpose, non-foundational model: YOLO. YOLO excels in object detection and can provide bounding boxes that serve as in...