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Quantifying the accuracy of automated structure segmentation in 4D CT images using a deformable image registration

Krishni Wijesooriya1, E Weiss, V Dill

  • 1Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23284, USA. kw5wx@virginia.edu

Medical Physics
|May 22, 2008
PubMed
Summary

Automated contouring in four-dimensional (4D) radiotherapy shows accuracy comparable to manual methods, especially for gross tumor volume (GTV). This deformable image registration technique offers reliable contour propagation across respiratory phases in CT scans.

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Area of Science:

  • Medical Physics
  • Radiotherapy Technology
  • Image Analysis

Background:

  • Four-dimensional (4D) radiotherapy integrates anatomical changes over time into treatment planning and delivery.
  • Automatic contouring on respiratory phase computed tomography (CT) datasets is crucial for 4D radiotherapy planning.
  • Deformable image registration is a key tool for propagating manual contours across respiratory phases.

Purpose of the Study:

  • To geometrically quantify the differences between automatically generated and manually drawn contours in 4D CT scans.
  • To evaluate the accuracy of deformable image registration for contour propagation in radiotherapy planning.

Main Methods:

  • Deformable image registration was used to map CT datasets from peak-inhale to other respiratory phases.
  • Manually drawn contours on one phase were automatically deformed to other phases using calculated displacement vector fields.
  • Volumetric, displacement, and surface congruence metrics were used to compare 692 auto-contoured structures with 692 manually drawn structures.

Main Results:

  • Automated and manual contouring methods showed similar trends, with smaller differences for gross tumor volume (GTV) compared to other structures.
  • For the GTV, fractional volumes agreed within 0.2+/-0.1, center of mass displacements within 0.5+/-1.5 mm, and surface congruence within 0.0+/-1.1 mm.
  • Surface congruence was less than 5 mm for 99% of GTVs, 94% of hearts and left lungs, 91% of right lungs, and 89% of esophagi.

Conclusions:

  • Automated contouring using deformable image registration agrees well with manual contouring, particularly for the GTV, within published interobserver variations.
  • The accuracy of auto-contoured structures is sufficient for clinical use, especially for the GTV.
  • Careful assessment of automatic algorithms is necessary, considering potential 4D CT artifacts.