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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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  1. Home
  3. Anatomy Consistent Segmentation Network For Joint Pet/ct Tumor Segmentation.
  1. Home
  3. Anatomy Consistent Segmentation Network For Joint Pet/ct Tumor Segmentation.

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Anatomy consistent segmentation network for joint PET/CT tumor segmentation.

Minghao Mao1, Yuxuan Qi1, Jingya Zhang2

  • 1School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China.

Artificial Intelligence in Medicine
|June 22, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an Anatomy-Consistent Segmentation Network (ACSN) for improved PET/CT tumor segmentation. The network disentangles features and calibrates uncertainty for more reliable clinical oncology applications.

Keywords:
Dempster-Shafer theoryDisentangled representation learningMultimodal fusion networkPET/CTTumor segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Multimodal PET/CT imaging is crucial for oncology, but tumor segmentation faces challenges with feature entanglement and unreliable uncertainty estimates.
  • Existing methods struggle to effectively integrate anatomical (CT) and functional (PET) data while quantifying segmentation confidence.

Purpose of the Study:

  • To develop an Anatomy-Consistent Segmentation Network (ACSN) that enhances multimodal tumor segmentation by addressing feature entanglement and improving uncertainty estimation.
  • To provide reliable uncertainty quantification for PET/CT tumor segmentation to aid clinical decision-making.

Main Methods:

  • Proposes an ACSN utilizing a dual-branch adversarial Feature Disentanglement Module (FDM) to separate shared anatomical features from PET and CT data.
  • Incorporates a Feature Fusion Module (FFM) with Multi-receptive field Segmentation Backbones (MSB) and a Layer-wise Uncertainty Calibrator (LUC) for calibrated multimodal fusion.
  • Employs Dempster-Shafer theory (DST) for integrating calibrated evidence from PET, CT, and fused representations to produce final tumor predictions.

    • Main Results:

    • ACSN demonstrates competitive 2D axial slice-wise segmentation performance on the AutoPET and Hecktor datasets.
    • The network provides calibrated uncertainty estimates that correlate with segmentation errors, indicating improved reliability.
    • The proposed Layer-wise Uncertainty Calibrator (LUC) effectively mitigates mis-calibration risks in uncertainty estimation.

    Conclusions:

    • ACSN offers a promising approach for uncertainty-aware PET/CT tumor segmentation in clinical oncology.
    • The method's ability to disentangle features and calibrate uncertainty can enhance diagnostic accuracy and quality control.
    • The developed ACSN framework has the potential to serve as valuable auxiliary information for oncologists using PET/CT imaging.