Automated Retinal Dysplasia Segmentation in Mouse Optical Coherence Tomography Scans Using a UNet-Based model (opens in new tab)

Optical coherence tomography (OCT) is the state-of-the-art non-invasive imaging technique for preclinical retinopathy studies. However, manual pathology annotations in mouse OCT scans are labour-intensive and susceptible to inter-rater variability. To alleviate these issues, we developed a neural network-based model for automated annotation of retinal dysplasia in mouse OCT scans. Our model was trained on 205 expert-annotated OCT stacks and validated on 40 unseen stacks with additional expert...