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Quantitative bone metastases analysis based on image segmentation

Y E Erdi1, J L Humm, M Imbriaco

  • 1Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA.

Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine
|September 18, 1997
PubMed
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A new semiautomated image segmentation program quantifies bone metastases more objectively and efficiently than manual methods. This Bone Scan Index (BSI) tool aids in assessing cancer prognosis and monitoring treatment response in prostate and breast cancer patients.

Area of Science:

  • Oncology
  • Medical Imaging
  • Quantitative Analysis

Background:

  • The extent of bone metastases is a critical prognostic indicator for prostate and breast cancer survival.
  • Current methods for quantifying bone metastases, such as the Bone Scan Index (BSI), rely on subjective visual estimation, leading to time-consuming and inconsistent results.

Purpose of the Study:

  • To develop and evaluate a semiautomated image segmentation program for objective and efficient quantitation of bone metastases from planar whole-body bone scans.
  • To improve upon the limitations of the current visual estimation-based Bone Scan Index.

Main Methods:

  • A semiautomated image segmentation program was developed, requiring user input of seed points in metastatic regions.
  • The algorithm uses a contrast-dependent threshold for region growing to delineate lesions.

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  • Lesion quantification incorporates bone mass data adjusted for patient demographics (race, sex, height, age) to calculate skeletal involvement.

    • Main Results:

    • The semiautomated system demonstrated a high correlation (r = 0.83) with conventional region-of-interest (ROI) methods.
    • The system consistently provided lower estimates of bone involvement compared to manual methods, attributed to improved resolution detection.
    • Delineation and quantitation time were reduced by at least half compared to manual ROI drawing.

    Conclusions:

    • Semiautomated image segmentation offers a more objective and time-efficient method for quantifying bone metastases.
    • This technique enhances the ability to detect subtle changes in follow-up scans, overcoming limitations of user variability in manual methods.
    • The system integrates physician expertise in lesion identification with objective algorithmic delineation for improved diagnostic accuracy.