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FluoroSAM: A Language-promptable Foundation Model for Flexible X-ray Image Segmentation.

Benjamin D Killeen1, Liam J Wang1, Blanca Iñígo1

  • 1Johns Hopkins University, Baltimore, MD 21218, USA.

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PubMed
Summary
This summary is machine-generated.

FluoroSAM enables flexible X-ray image segmentation using natural language prompts. This language-aligned foundation model (LFM) advances precision medicine by segmenting anatomical structures and tools in diverse X-ray images.

Keywords:
deep learningfluoroscopymachine learningmedical imaging AImultimodal foundation modelradiologysegment anything

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Existing medical image analysis models are often task-specific, limiting their broad applicability.
  • Language-aligned foundation models (LFMs) show promise for automated image analysis but require extensive data.
  • X-ray imaging presents unique challenges due to high variability in appearance and applications.

Purpose of the Study:

  • To develop a language-promptable foundation model for comprehensive X-ray image analysis.
  • To enable flexible human-in-the-loop workflows in diagnostic and interventional medicine.
  • To address the data variability and limited annotations in X-ray imaging.

Main Methods:

  • Introduced FluoroSAM, a variant of the Segment-Anything Model trained on 3 million synthetic X-ray images.
  • Incorporated vector quantization (VQ) of text embeddings for natural language prompt integration.
  • Utilized diverse anatomies, imaging geometries, and viewing angles with pseudo-ground truth masks for organs and tools.

Main Results:

  • FluoroSAM successfully segments numerous anatomical structures and tools from X-ray images using natural language prompts.
  • Demonstrated quantitative performance on real X-ray data.
  • Showcased FluoroSAM's utility in various applications, facilitating human-machine interaction.

Conclusions:

  • FluoroSAM advances language-promptable X-ray image segmentation.
  • The model enhances flexibility for human-in-the-loop workflows in medical imaging.
  • FluoroSAM is a key enabler for improved X-ray image acquisition and analysis.