Motion correction in retinal optical coherence tomography imaging using deep learning registration

Optical coherence tomography (OCT) retinal volumes are prone to motion artifacts due to the movement of the eye during acquisition. Current retrospective motion correction algorithms are either computationally expensive or limited to pair-wise formulations, based on registration of consecutive slices (B-scans). This type of approach can lead to errors when individual B-scans contain artifacts or lack sufficient signal. Instead, we propose a framework, based on unsupervised deep learning, that corrects motion by aligning groups of consecutive B-scans. The network architecture is fully-convolutional and, thus, it can perform inference on the entire OCT volume, even though it was trained on groups of smaller size. Moreover, we improved performance by inferring in a multi-shot recurrent manner, which was further leveraged by a novel data augmentation technique. We used an exhaustive search algorithm (brute-force) to compare the proposed method against, both quantitatively and qualitatively based on visual assessment. In a dataset of 146 (training: 106, validation: 40) macula and optic disc volumes from 19 healthy subjects, our best performing configuration achieved 72% reduction in registration errors compared to the exhaustive search algorithm, with a computation time of 2.35 seconds. These results demonstrated that our framework has the potential to provide a fast and robust solution, based on deep learning registration, for the motion correction of OCT images.