Performance Evaluation of Automatic Anatomy Segmentation Algorithm on Repeat or Four-Dimensional Computed Tomography Images Using Deformable Image Registration Method

Abstract

Purpose-Auto-propagation of anatomical region-of-interests (ROIs) from the planning CT to daily CT is an essential step in image-guided adaptive radiotherapy. The goal of this study was to quantitatively evaluate the performance of the algorithm in typical clinical applications.Method and Materials-We previously adopted an image intensity-based deformable registration algorithm to find the correspondence between two images. In this study, the ROIs delineated on the planning CT image were mapped onto daily CT or four-dimentional (4D) CT images using the same transformation. Post-processing methods, such as boundary smoothing and modification, were used to enhance the robustness of the algorithm. Auto-propagated contours for eight head-and-neck patients with a total of 100 repeat CTs, one prostate patient with 24 repeat CTs, and nine lung cancer patients with a total of 90 4D-CT images were evaluated against physician-drawn contours and physician-modified deformed contours using the volume-overlap-index (VOI) and mean absolute surface-to-surface distance (ASSD).Results-The deformed contours were reasonably well matched with daily anatomy on repeat CT images. The VOI and mean ASSD were 83% and 1.3 mm when compared to the independently drawn contours. A better agreement (greater than 97% and less than 0.4 mm) was achieved if the physician was only asked to correct the deformed contours. The algorithm was robust in the presence of random noise in the image.Conclusion-The deformable algorithm may be an effective method to propagate the planning ROIs to subsequent CT images of changed anatomy, although a final review by physicians is highly recommended.Correspondence and reprint requests to:

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