A fast and scalable method for quality assurance of deformable image registration on lung CT scans using convolutional neural networks

Purpose: To develop and evaluate a method to automatically identify and quantify deformable image registration (DIR) errors between lung computed tomography (CT) scans for quality assurance (QA) purposes. Methods: We propose a deep learning method to flag registration errors. The method involves preparation of a dataset for machine learning model training and testing, design of a three-dimensional (3D) convolutional neural network architecture that classifies registrations into good or poor classes, and evaluation of a metric called registration error index (REI) which provides a quantitative measure of registration error. Results: Our study shows that, despite having limited number of training images available (10 CT scan pairs for training and 17 CT scan pairs for testing), the method achieves 0.882 AUC-ROC on the test dataset. Furthermore, the combined standard uncertainty of the estimated REI by our model lies within ± 0.11 (± 11% of true REI value), with a confidence level of approximately 68%. Conclusions: We have developed and evaluated our method using original clinical registrations without generating any synthetic/simulated data. Moreover, test data were acquired from a different environment than that of training data, so that the method was validated robustly. The results of this study showed that our algorithm performs reasonably well in challenging scenarios.