# Read-out Basis Adapter

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.