  Segmenting anatomical structures such as the photoreceptor layer in retinal
optical coherence tomography (OCT) scans is challenging in pathological
scenarios. Supervised deep learning models trained with standard loss functions
are usually able to characterize only the most common disease appeareance from
a training set, resulting in suboptimal performance and poor generalization
when dealing with unseen lesions. In this paper we propose to overcome this
limitation by means of an augmented target loss function framework. We
introduce a novel amplified-target loss that explicitly penalizes errors within
the central area of the input images, based on the observation that most of the
challenging disease appeareance is usually located in this area. We
experimentally validated our approach using a data set with OCT scans of
patients with macular diseases. We observe increased performance compared to
the models that use only the standard losses. Our proposed loss function
strongly supports the segmentation model to better distinguish photoreceptors
in highly pathological scenarios.
