Evaluation of TransUNet for the Segmentation of Retinal Structures in OCT-A

Optical coherence tomography angiography (OCT-A) is a novel, noninvasive technology for visualizing retinal structures. Segmentation of these structures can indicate ophthalmic diseases such as diabetic retinopathy or glaucoma to aid diagnosis. However, limited data availability and artifacts make this task challenging. Adapted from other domains, transformer-based models yield promising results in medical image segmentation, challenging U-Net as the de facto standard method. This work evaluates TransUNet, a hybrid transformer and convolutionbased architecture against U-Net in the task of segmenting retinal structures in OCT-A in a multifaceted comparison. Robustness under a reduced training volume and the effect of varying degrees of data augmentation, including noise simulations specific to OCT-A, are included as further aspects of comparison. Although no significant advantage in overall segmentation performance was found, TransUNet outperformed U-Net in the data-reduced setting and showed a better aptitude to capitalize on augmented data.