Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate Thin Plate Spline Mappings
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Regularization in the selection of radial basis function centers
Neural Computation
Maximizing the Predictivity of Smooth Deformable Image Warps through Cross-Validation
Journal of Mathematical Imaging and Vision
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The aim of this study is to automatically assess reendothelialization of stents at an accuracy of down to a few microns by analyzing endovascular optical coherence tomography (OCT) sequences. Vessel wall and struts are automatically detected and complete distance map is then computed from sparse distances measured between wall and struts by thin-plate spline (TPS) interpolation. A reendothelialization score is mapped onto the geometry of the coronary artery segment. Accuracy and robustness are increased by taking into account the inhomogeneity of datapoints and integrating in the same framework orthogonalized forward selection of support points, optimal selection of regularization parameters by generalized cross-validation (GCV) and rejection of detection outliers. The comparison against manual expert measurements for a phantom study and 12 in vivo stents demonstrates no significant discordance with variability of the order of the strut thickness.