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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Semi-automated CT segmentation using optic flow and Fourier interpolation techniques.

Tzung-Chi Huang1, Geoffrey Zhang, Thomas Guerrero

  • 1Department of Radiation Oncology, The University of Texas Southwestern Medical Center, 5801 Forest Park Road, Dallas, TX 75390-9183, USA.

Computer Methods and Programs in Biomedicine
|October 10, 2006
PubMed
Summary

This study introduces semi-automated segmentation for radiotherapy planning, reducing contouring time. The method accurately maps contours across CT slices, similar to manual variations, but requires adjustments for topological differences.

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Area of Science:

  • Medical imaging
  • Radiotherapy
  • Computational anatomy

Background:

  • Manual contouring of tumor volumes and anatomical structures in radiotherapy treatment planning is time-consuming for clinicians.
  • Accurate segmentation is crucial for precise dose calculation and effective treatment delivery.

Purpose of the Study:

  • To evaluate a semi-automated segmentation technique for contouring anatomical structures and tumors in CT images for radiotherapy.
  • To reduce the time and variability associated with manual contouring in treatment planning.

Main Methods:

  • A semi-automated approach using manual input of high curvature points on a CT slice.
  • Fourier interpolation to complete contours on initial slices.
  • Optical flow, a deformable image registration technique, to map contours to adjacent slices.

Main Results:

  • Successful application of the technique for contouring anatomical structures and tumors.
  • Maximum contour mapping difference of 6 pixels, comparable to inter-observer variability.
  • Identified failure cases when contour topology differs significantly between slices.

Conclusions:

  • The semi-automated segmentation method offers a viable alternative to manual contouring, with accuracy comparable to clinical standards.
  • A strategy of delineating sparse slices and bidirectional mapping is proposed to overcome topological challenges.
  • Further development could enhance efficiency and applicability in radiotherapy treatment planning.