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Related Experiment Video

Updated: May 16, 2026

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer
06:51

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

Published on: July 21, 2018

Lung tumor segmentation in PET images using graph cuts.

Cherry Ballangan1, Xiuying Wang, Michael Fulham

  • 1Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Australia. cherry@it.usyd.edu.au

Computer Methods and Programs in Biomedicine
|November 14, 2012
PubMed
Summary

This study introduces an improved lung tumor segmentation method using positron emission tomography (PET) and graph cuts. The technique enhances accuracy in measuring tumor size and spread, aiding better patient management decisions for non-small cell lung cancer (NSCLC).

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Biology

Background:

  • Accurate tumor segmentation in Positison Emission Tomography (PET) is crucial for precise tumor size and extension measurements, improving patient management.
  • Visual assessment alone has limitations in accurately defining tumor boundaries and extent.
  • Existing segmentation methods struggle with heterogeneous tumors and avoiding leakage into surrounding tissues.

Purpose of the Study:

  • To develop an improved lung tumor segmentation method using PET.
  • To enhance the accuracy of tumor measurements beyond visual assessment.
  • To improve patient management decisions for non-small cell lung cancer (NSCLC) through better segmentation.

Main Methods:

  • Proposed a novel segmentation energy function for the graph cuts technique.

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  • Integrated analysis of tumor voxels, a standardized uptake value (SUV) cost function, and a monotonic downhill SUV feature.
  • Evaluated the method on 42 clinical PET volumes from NSCLC patients.

    • Main Results:

    • The proposed method demonstrated improved lung tumor segmentation compared to visual assessment.
    • Achieved better performance than traditional region growing, watershed, fuzzy-c-means, region-based active contour, and tumor customized downhill methods.
    • The monotonic downhill feature effectively prevented segmentation leakage into surrounding tissues.
    • The SUV cost function improved boundary definition and handled heterogeneous tumors.

    Conclusions:

    • The developed graph cuts-based segmentation energy function significantly enhances lung tumor segmentation in PET.
    • This approach offers a more accurate and robust method for tumor measurement, outperforming existing techniques.
    • The findings suggest a potential for improved clinical decision-making in NSCLC management.