mlsanityData to 14 Oct 2026

Autonomous Skeletal Landmark Localization towards Agentic C-Arm Control

arXiv 2604.18740 ↗First posted 20 Apr 2026cs.CVcited by 0

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Abstract (arXiv)

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 dataset. Each X-ray in both datasets is paired with several skeletal landmarks. We fine-tuned two MLLMs and tasked them with retrieving the closest landmarks from each X-ray. Quantitative evaluations of landmark localization were performed and compared against a leading DL approach. We further conducted qualitative experiments demonstrating: (1) how an MLLM can correct an initially incorrect prediction through reasoning, and (2) how the MLLM can sequentially navigate the C-arm toward a target location. Results: On both datasets, fine-tuned MLLMs demonstrate competitive performance across all localization tasks when compared with the DL approach. In the qualitative experiments, the MLLMs provide evidence of reasoning and spatial awareness. Conclusion: This study shows that fine-tuned MLLMs achieve accurate skeletal landmark localization and hold promise for agentic autonomous C-arm control. Our code is available athttps://github.com/marszzibros/C-arm-localization-LLMs.git

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 18 references that are in the map. First posted on arXiv 20 Apr 2026.

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10 of 18 references.

References · 10

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  3. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,848
  4. 2022MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answeringcited by 543
  5. 2024MMedAgent: Learning to Use Medical Tools with Multi-modal Agentcited by 109
  6. 2024LoRA vs Full Fine-tuning: An Illusion of Equivalencecited by 109
  7. 2018DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedurescited by 55
  8. 2023Shape-based pose estimation for automatic standard views of the kneecited by 1
  9. 2025C-arm Guidance: A Self-supervised Approach To Automated Positioning During Stroke Thrombectomycited by 1
  10. 2025Automated C-Arm Positioning via Conformal Landmark Localizationcited by 1

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