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Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Segment Anything Model (SAM) and Medical SAM (MedSAM) for Lumbar Spine MRI.

Christian Chang1, Hudson Law2, Connor Poon3

  • 1Punahou School, Honolulu, HI 96822, USA.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
Summary

Zero-shot AI models like MedSAM show promise for segmenting lumbar spine MRI scans, offering faster adaptation for low back pain research despite not matching nnU-Net performance.

Keywords:
artificial intelligencedeep learningimage segmentationintervertebral discpromptablevertebral bodyvision transformerzero shot

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Biomedical Engineering

Background:

  • Lumbar spine Magnetic Resonance Imaging (MRI) is crucial for evaluating intervertebral discs (IVDs) and vertebral bodies (VBs) in low back pain.
  • Accurate segmentation of IVDs and VBs provides quantitative insights into shape and volume, aiding diagnosis and research.
  • Current segmentation methods can be labor-intensive and require extensive training data.

Purpose of the Study:

  • To evaluate the performance of "zero-shot" deep learning models, Segment Anything Model (SAM) and medical SAM (MedSAM), for segmenting lumbar spine IVDs and VBs on MRI.
  • To compare the segmentation accuracy of SAM and MedSAM against the established nnU-Net model.
  • To assess the feasibility of using zero-shot models for quantitative analysis in low back pain research.

Main Methods:

  • A cadaveric study utilizing 82 donor spines with lumbar MRI scans.
  • Manual segmentation performed by experts served as the ground truth.
  • Two readers applied SAM and MedSAM using point and bounding box annotations on MRI ROIs.
  • Quantitative comparison using Dice score, sensitivity, and specificity against ground truth and nnU-Net.

Main Results:

  • MedSAM demonstrated higher mean Dice scores (0.79 for IVDs, 0.88 for VBs) compared to SAM (0.64 for IVDs, 0.83 for VBs), with statistically significant differences (p < 0.001).
  • Both SAM and MedSAM showed lower performance than nnU-Net (0.99 for both IVD and VB).
  • MedSAM provided more consistent segmentation results than SAM, and sensitivity values also favored MedSAM.

Conclusions:

  • Zero-shot deep learning models, particularly MedSAM, are feasible for segmenting lumbar spine MRI, offering potential for rapid adaptation.
  • While current zero-shot models do not match the performance of specialized models like nnU-Net, their lack of need for training data is a significant advantage.
  • Further validation and development of generalizable segmentation models for lumbar spine MRI can enhance diagnostics, patient follow-up, and low back pain research, potentially reducing costs through automated analysis.