The registration quality assessment is usually given to human experts, which is time-consuming. The commonly used Dice score only focuses on the error at the edge of the tissue, which is difficult to assess the registration result within the tissue. To address these issues, this paper proposed a method to predict registration errors based on machine learning(PREML) in lung CT images. This method firstly constructed three types of features, such as deformation field statistical features, deformation field physiologically realistic features and image similarity features, then expanded the number of features by pooling, and finally used random forest regression to predict non-rigid registration errors. Moreover, it used an adaptive random perturbation to simulate the spatial distribution of lung registration errors to further improve the capability of error characterization of statistical features. The proposed method achieved a mean absolute error of 1.245±2.500 mm from ground truth on lung CT image datasets, outperforming the baseline method. The results show that PREML method has the advantages of high accuracy and robustness, enhancing the safety and effectiveness of registration algorithms in clinical applications.

[2022-12-12] Accepted Paper /doc/2022.09.0488/v1

[2023-06-05] Printed Article /doc/2022.09.0488/v2