OC-0068: Can atlas-based auto-contouring ever be perfect?
Bas Schipaanboord, Johan van Soest, Djamal Boukerroui, Tim Lustberg, Wouter van Elmpt, Timor Kadir, A. Dekker, Mark J. Gooding · Radiotherapy and Oncology · 2016
quantitative measures such as the target registration error can be used during commissioning, such measures are not fully spatial and too user intensive in clinical practice.Therefore, we propose a fully automatic and quantitative approach to DIR quality assessment including multiple measures of numerical robustness and biological plausibility. Material and Methods:Ten head and neck cancer patients who received weekly repeat CT (rCT) scans were included.Per patient, the first rCT was deformable registered (using Bspline DIR algorithm) to the planning CT.The ground-truth deformation error of this registration was derived using the scale invariant feature transform (SIFT), which automatically extracts and matches stable and prominent points between two images.Moreover, complementary quantitative and spatial measures of registration quality were calculated.Numerical robustness was derived from the inverse consistency error (ICE), transitivity error (TE), and distance discordance metric (DDM).For the TE calculations a third CT was used.The DDM was calculated using five CT sets per patient.Biological plausibility was based on the deformation vector field between the planning CT and rCT.Relative deformation threshold values were set based on physical tissue characteristics: 5% for bone and 50% for soft tissues.All measures were evaluated in bone and soft tissue structures and compared against the ground-truth deformation error.Results: On average, SIFT detected 133 matching points scattered throughout the planning CT, with a mean (max) registration error of 1.6 (8.3) mm.Our combined and fully spatial DIR evaluation approach, including the ICE, TE and DDM, resulted in a mean (max) error of respectively 0.6 (2.0), 0.7 (2.7), and 0.6 (2.7) mm within the external body contour, averaged over all patients.The largest errors were detected in homogeneous regions and near air cavities.Furthermore, 87% of the bone and 2% of the soft tissue voxels were classified as unrealistic deformations.Figure 1 shows the planning CT, DDM, tissue deformation, and error volume histograms of the ICE, TE, and DDM of the body contour of one patient. Conclusion:The combination of multiple automatic DIR quality measures highlighted areas of concern within the registration.While current methods on DIR evaluation, such as visual inspection and target registration error are timeconsuming, local, and qualitative, this approach provided an automated, fully spatial and quantitative tool for clinical assessment of patient-specific DIR even in image regions with limited contrast.