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Updated: Jun 5, 2026

Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates
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Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates

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An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation.

Xiaofeng Liu1, Qianru Zhang1, Thibault Marin1

  • 1Department of Radiology and Biomedical Imaging, Yale Biomedical Imaging Institute, Yale University, New Haven, CT, USA.

Arxiv
|June 4, 2026
PubMed
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This summary is machine-generated.

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A new foundation model for PET/CT imaging integrates anatomical and metabolic data early for improved tumor segmentation. This open-source tool enhances deep learning efficiency, reducing the need for extensive manual annotations in oncology.

Area of Science:

  • Oncologic Imaging
  • Medical Image Analysis
  • Deep Learning

Background:

  • Synergistic interpretation of computed tomography (CT) and positron emission tomography (PET) is crucial for oncologic imaging.
  • Current deep learning models for PET/CT are often task-specific, single-center, and delay cross-modal interaction.

Purpose of the Study:

  • To develop an open-source, multi-center, whole-body FDG PET/CT foundation model.
  • To improve early spatial correspondence and cross-modal interaction between PET and CT data.
  • To enhance label efficiency and representation learning for PET/CT tumor segmentation.

Main Methods:

  • Utilized 4,997 harmonized scans from four public datasets.
  • Employed hierarchical UNet-shaped backbones with early channel-wise concatenation for feature interaction.
Keywords:
Deep LearningFoundation ModelMulti-CenterPET/CTTumor Segmentation

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Last Updated: Jun 5, 2026

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  • Introduced a masked autoencoding objective with zero-mean imputation and weighted global reconstruction loss.

    • Main Results:

    • Demonstrated strong label efficiency, achieving performance comparable to full-dataset training with only 10% labeled data on AutoPET lesion segmentation.
    • Achieved higher Dice scores with joint PET/CT pretraining compared to separated-modality pretraining under 5-shot linear probing.
    • Showcased effective cross-modality representation learning for PET/CT tumor segmentation.

    Conclusions:

    • The proposed multi-center foundation model offers significant label efficiency for PET/CT tumor segmentation.
    • Provides a robust, open-source basis for advancing automated oncologic imaging.
    • Reduces the clinical need for large-scale manual annotations in medical imaging.