  Automated drusen segmentation in retinal optical coherence tomography (OCT)
scans is relevant for understanding age-related macular degeneration (AMD) risk
and progression. This task is usually performed by segmenting the top/bottom
anatomical interfaces that define drusen, the outer boundary of the retinal
pigment epithelium (OBRPE) and the Bruch's membrane (BM), respectively. In this
paper we propose a novel multi-decoder architecture that tackles drusen
segmentation as a multitask problem. Instead of training a multiclass model for
OBRPE/BM segmentation, we use one decoder per target class and an extra one
aiming for the area between the layers. We also introduce connections between
each class-specific branch and the additional decoder to increase the
regularization effect of this surrogate task. We validated our approach on
private/public data sets with 166 early/intermediate AMD Spectralis, and 200
AMD and control Bioptigen OCT volumes, respectively. Our method consistently
outperformed several baselines in both layer and drusen segmentation
evaluations.
