Evaluation of Deformable Image Registration-Based Contour Propagation From Planning CT to Cone-Beam CT

PURPOSE We evaluated the performance of organ contour propagation from a planning computed tomography to cone-beam computed tomography with deformable image registration by comparing contours to manual contouring. MATERIALS AND METHODS Sixteen patients were retrospectively identified based on showing considerable physical change throughout the course of treatment. Multiple organs in the 3 regions (head and neck, prostate, and pancreas) were evaluated. A cone-beam computed tomography from the end of treatment was registered to the planning computed tomography using rigid registration, followed by deformable image registration. The contours were copied on cone-beam computed tomography image sets using rigid registration and modified by 2 radiation oncologists. Contours were compared using Dice similarity coefficient, mean surface distance, and Hausdorff distance. RESULTS The mean physician-to-physician Dice similarity coefficient for all organs was 0.90. When compared to each physician's contours, the overall mean for rigid was 0.76 ( P < .001), and it was improved to 0.79 ( P < .001) for deformable image registration. Comparing deformable image registration to physicians resulted in a mean Dice similarity coefficient of 0.77, 0.74, and 0.84 for head and neck, prostate, and pancreas groups, respectively; whereas, the physician-to-physician mean agreement for these sites was 0.87, 0.90, and 0.93 ( P < .001, for all sites). The mean surface distance for physician-to-physician contours was 1.01 mm, compared to 2.58 mm for rigid-to-physician contours and 2.24 mm for deformable image registration-to-physician contours. The mean physician-to-physician Hausdorff distance was 11.32 mm, and when compared to any physician's contours, the mean for rigid and deformable image registration was 12.1 mm and 12.0 mm ( P < .001), respectively. CONCLUSION The physicians had a high level of agreement via the 3 metrics; however, deformable image registration fell short of this level of agreement. The automatic workflows using deformable image registration to deform contours to cone-beam computed tomography to evaluate the changes during treatment should be used with caution.