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Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body being...

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Multi-modal segment anything model (mmSAM) for tumor segmentation in multi-tracer oncologic PET/CT.

Zhijie Fang1, Yibin Liu1, Tao Tan2

  • 1Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China.

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|May 17, 2026
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Summary

A novel multi-modal Segment Anything Model (mmSAM) shows promise for lesion segmentation in PET/CT scans. Fine-tuning mmSAM significantly improves accuracy for cross-tracer studies in oncologic imaging.

Keywords:
Multi-modalPET/CTSegment anything model (SAM)Tumor segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncologic Imaging

Background:

  • Accurate lesion segmentation is crucial for dosimetry in targeted radionuclide therapy.
  • Whole-body multi-tracer PET/CT imaging provides valuable diagnostic information.
  • Existing segmentation methods may have limitations in multi-modal and cross-tracer scenarios.

Purpose of the Study:

  • To propose and evaluate a novel 2D multi-modal Segment Anything Model (mmSAM) for lesion segmentation.
  • To assess the performance of mmSAM in whole-body multi-tracer PET/CT images.
  • To investigate the transferability and effectiveness of mmSAM with and without fine-tuning on different tracers.

Main Methods:

  • Utilized the AutoPET 2024 dataset with 18F-PSMA, 18F-FDG, and 68Ga-PSMA PET/CT images.
  • Trained and validated the mmSAM on the 18F-PSMA dataset, incorporating both PET and CT inputs.
  • Evaluated mmSAM's transferability on 18F-FDG and 68Ga-PSMA datasets, with and without fine-tuning.
  • Compared mmSAM against standard 2D SAM, 3D nnUNet, and thresholding methods.
  • Quantified performance using metrics like Dice, HD95, SUVmean error, MTV error, TPR, PPV, and FDR.

Main Results:

  • mmSAM outperformed other methods on the primary 18F-PSMA dataset (Dice: 0.76, HD95: 1.92 mm).
  • Without fine-tuning, mmSAM showed varying performance on cross-tracer datasets (18F-FDG Dice: 0.61, 68Ga-PSMA Dice: 0.77).
  • Fine-tuning significantly improved mmSAM's performance on cross-tracer datasets (18F-FDG Dice: 0.65, 68Ga-PSMA Dice: 0.81).
  • mmSAM consistently achieved high TPR (100%) across all evaluations.

Conclusions:

  • The proposed mmSAM demonstrates significant potential for lesion segmentation in multi-tracer oncologic PET/CT imaging.
  • Fine-tuning is a critical step to enhance segmentation accuracy when applying mmSAM across different radiotracers.
  • mmSAM offers a promising tool for improving dosimetry and treatment planning in targeted radionuclide therapy.