Semi-automated CT segmentation using optic flow and Fourier interpolation techniques

  • Authors:
  • Tzung-Chi Huang;Geoffrey Zhang;Thomas Guerrero;George Starkschall;Kan-Ping Lin;Ken Forster

  • Affiliations:
  • Department of Radiation Oncology, The University of Texas Southwestern Medical Center, 5801 Forest Park Road, Dallas, TX 75390-9183, United States;Department of Radiation Oncology, The University of Texas Southwestern Medical Center, 5801 Forest Park Road, Dallas, TX 75390-9183, United States;U.T.MD Anderson Cancer Center, Houston, TX, United States;U.T.MD Anderson Cancer Center, Houston, TX, United States;Chung-Yuan University, Taipei, Taiwan;Department of Radiation Oncology, The University of Texas Southwestern Medical Center, 5801 Forest Park Road, Dallas, TX 75390-9183, United States

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2006

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Abstract

In radiotherapy treatment planning, tumor volumes and anatomical structures are manually contoured for dose calculation, which takes time for clinicians. This study examines the use of semi-automated segmentation of CT images. A few high curvature points are manually drawn on a CT slice. Then Fourier interpolation is used to complete the contour. Consequently, optical flow, a deformable image registration method, is used to map the original contour to other slices. This technique has been applied successfully to contour anatomical structures and tumors. The maximum difference between the mapped contours and manually drawn contours was 6 pixels, which is similar in magnitude to difference one would see in manually drawn contours by different clinicians. The technique fails when the region to contour is topologically different between two slices. A solution is recommended to manually delineate contours on a sparse subset of slices and then map in both directions to fill the remaining slices.