"Accuracy Estimation for Medical Image Registration Using Regression Forests"
Hessam Sokooti, Gorkem Saygili, Ben Glocker, Boudewijn P.F. Lelieveldt and Marius Staring
This paper reports a new automatic algorithm to estimate the misregistration in a quantitative manner. A random regression forest is constructed, predicting the local registration error. The forest is built using local and modality independent features related to the registration precision, the transformation model and intensity-based similarity after registration. The forest is trained and tested using manually annotated corresponding points between pairs of chest CT scans. The results show that the mean absolute error of regression is 0.72 ± 0.96 mm and the accuracy of classiffication in three classes (correct, poor and wrong registration) is 93.4%, comparing favorably to a competing method. In conclusion, a method was proposed that for the first time shows the feasibility of automatic registration assessment by means of regression, and promising results were obtained.