To perform robust quality control of medical imaging registration, we propose a method that can QC co-registration for multiple organs, without the restriction of the modalities. A rule-based image synthesis pipeline is used to generate random contrasts and shapes as training images. ResNet34 is trained to predict image alignment. Two MRI datasets with the spine or brain as subjects are used as external validation sets. The proposed model trained with synthetic images is validated on either one of the real MRI datasets and outperforms the same model trained on the other MRI dataset, which shows better generalizability.
This abstract and the presentation materials are available to members only; a login is required.