Autonomous Skeletal Landmark Localization towards Agentic C-Arm Control
Purpose: Automated C-arm positioning ensures timely treatment in patients requiring emergent interventions. When a conventional Deep Learning (DL) approach for C-arm control fails, clinicians must revert to manual operation, resulting in additional delays. Consequently, an agentic C-arm control framework based on multimodal large language models (MLLMs) is highly desirable, as it can incorporate clinician feedback and use reasoning to make adjustments toward more accurate positioning. Skeletal landmark localization is essential for C-arm control, and we investigate adapting MLLMs for autonomous landmark localization. Methods: We used an annotated synthetic X-ray dataset and a real X-ray d...
| Authors | Jay Jung, Ahmad Arrabi, Jax Luo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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