Browsing by Subject "Vessel segmentation"

Background: Target Registration Error (TRE) calculated based on selected anatomical landmarks is commonly known as the only trustable way to evaluate DIR accuracy. However, manual landmark pair selection is labor intensive and subjected to observer variability. Currently, no DIR benchmark datasets are available for Liver CTs. Manually selected landmark pairs have limited DIR evaluation power due to inadequate landmarks quantity (i.e., ~5 landmark pairs per dataset1 for liver CTs) and positional accuracy. For the purpose of liver DIR verification, there is a great need to establish a large quantity of landmark pairs with good positional accuracy.Purpose: An image processing procedure was developed in this study to automatically and precisely detect landmark pairs on corresponding vessel bifurcations between pairs of intra-patient CT images. With high positional accuracy, the generated landmark pairs can be used to evaluate deformable image registration (DIR) methods quantitatively for liver CTs. Methods: Landmark pairs were detected within the liver between pairs of contrast-enhanced CT scans for 32 patients. For each case, the liver vessel tree was automatically segmented in one image. Landmarks were automatically detected on vessel bifurcations. The corresponding landmarks in the second image were placed using a parametric DIR method (pTVreg). Manual validation was applied to reject outliers and adjust the landmarks’ positions to account for vessel segmentation uncertainty caused by the inconsistent image quality. Landmark pairs' positional accuracy of the procedure was evaluated using digital phantoms on target registration errors (TREs). Results: On average, ~71 landmark pairs per case were detected after manual outlier rejection. The proposed procedure increased the quantity of liver landmark pairs by ~10 times compared to the reported in the literature. A fully manual spot check showed that the reported procedure performed better than or as good as human at landmark pairs positional accuracy. Measured in the digital phantoms, the mean and standard deviation of TREs were 0.67 ± 0.48 mm with 99% of landmark pairs having TREs smaller than 2mm. Conclusion: A large number of liver landmark pairs with high positional accuracy were detected in contrasted enhanced CT image pairs using the reported method. The detected landmark pairs can be used for the quantitative evaluation of DIR methods.