# Retinal Vessel Segmentation

The interactive segmentation model SEEM performs poorly on OCTA retinal vessels out of the box, reaching only 75.83% Dice on large vessels with a frozen encoder, and every candidate mask it can produce is read from the same quarter-resolution feature maps, too coarse for vessels one to three pixels wide. The Read-out Basis Adapter (RBA) adds 32 half-resolution basis maps at the read-out, which each candidate accesses through a zero-initialised projection, for 0.47M trainable parameters. On OCTA-500 3 mm large vessels, averaged over three seeds, it reaches 91.93% Dice and 85.16% IoU, 1.72 and 2.90 points above Conv-LoRA, with gains concentrated on thin vessels and edges and no significant difference on the FAZ.

The interactive segmentation model SEEM performs poorly on OCTA retinal vessels out of the box, reaching only 75.83% Dice on large vessels with a frozen encoder, and every candidate mask it can produce is read from the same quarter-resolution feature maps, too coarse for vessels one to three pixels wide. The Read-out Basis Adapter (RBA) adds 32 half-resolution basis maps at the read-out, which each candidate accesses through a zero-initialised projection, for 0.47M trainable parameters. On OCTA-500 3 mm large vessels, averaged over three seeds, it reaches 91.93% Dice and 85.16% IoU, 1.72 and 2.90 points above Conv-LoRA, with gains concentrated on thin vessels and edges and no significant difference on the FAZ.

Retinal disease grading has mostly relied on raw color fundus photographs, where low-contrast lesions and thin vessels are easily lost to illumination and noise. F2D-RetinaNet sends an enhanced green channel and a vessel map through two separate branches and fuses them into one grading decision. On APTOS 2019 five-grade diabetic retinopathy staging it reaches 93.32% accuracy, 3.25 points above the RGB-only baseline, and 87.14% on the external DREAM-RFI set after overlapping images were excluded.

Retinal disease grading has mostly relied on raw color fundus photographs, where low-contrast lesions and thin vessels are easily lost to illumination and noise. F2D-RetinaNet sends an enhanced green channel and a vessel map through two separate branches and fuses them into one grading decision. On APTOS 2019 five-grade diabetic retinopathy staging it reaches 93.32% accuracy, 3.25 points above the RGB-only baseline, and 87.14% on the external DREAM-RFI set after overlapping images were excluded.