  Fundus photography has routinely been used to document the presence and
severity of retinal degenerative diseases such as age-related macular
degeneration (AMD), glaucoma, and diabetic retinopathy (DR) in clinical
practice, for which the fovea and optic disc (OD) are important retinal
landmarks. However, the occurrence of lesions, drusen, and other retinal
abnormalities during retinal degeneration severely complicates automatic
landmark detection and segmentation. Here we propose HBA-U-Net: a U-Net
backbone enriched with hierarchical bottleneck attention. The network consists
of a novel bottleneck attention block that combines and refines self-attention,
channel attention, and relative-position attention to highlight retinal
abnormalities that may be important for fovea and OD segmentation in the
degenerated retina. HBA-U-Net achieved state-of-the-art results on fovea
detection across datasets and eye conditions (ADAM: Euclidean Distance (ED) of
25.4 pixels, REFUGE: 32.5 pixels, IDRiD: 32.1 pixels), on OD segmentation for
AMD (ADAM: Dice Coefficient (DC) of 0.947), and on OD detection for DR (IDRiD:
ED of 20.5 pixels). Our results suggest that HBA-U-Net may be well suited for
landmark detection in the presence of a variety of retinal degenerative
diseases.
