RegQCNET: Deep Quality Control for Image-to-template Brain MRI Registration

Registration of one or several brain image(s) onto a common reference space defined by a template is a necessary prerequisite for many image processing tasks, such as brain structure segmentation or extraction of image-derived functional features. Manual assessment of the registration is a tedious and time-consuming task, especially when a large amount of data is involved. An automated and reliable quality control (QC) is thus mandatory. Moreover, the computation time of the QC must be also compatible with the processing of massive datasets. Therefore, deep neural network approaches appear as a method of choice to automatically assess registration quality. In the current study, a compact 3D CNN is introduced to quantitatively predict the amplitude of a registration mismatch between the registered image and the reference template. This quantitative estimation of registration error is expressed using metric unit system. Therefore, a meaningful task-specific threshold can be defined by the user in order to distinguish usable and non-usable images. Results have shown that the proposed deep learning QC is robust, fast and accurate to estimate registration error in processing pipeline.