Abstract: In this work, we compared the performance of 2D and 3D versions of three state-of-the-art deep neural networks on segmenting the retinal external limiting membrane (ELM) using a publicly available image dataset of spectral-domain optical coherence tomography (OCT) scans. Based on our results, 3D networks generally outperformed the 2D networks in Dice coefficient, mean surface distance and false positive rate but lagged behind in Hausdorff distance. 3D networks also produce smoother surfaces based on mean surface curvedness.

Keywords: 2D vs. 3D Image Segmentation, Machine Learning, Annotation, External Limiting Membrane, OCT.