# An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans

## TL;DR

This paper introduces an amplified-target loss function for deep learning models to improve segmentation of photoreceptor layers in pathological OCT scans, especially in challenging central regions.

## Contribution

The novel amplified-target loss explicitly emphasizes errors in the central image area, enhancing segmentation accuracy in diseased retinal OCT scans.

## Key findings

- Improved segmentation accuracy over standard loss functions.
- Better generalization to unseen lesions.
- Enhanced detection of photoreceptors in pathological cases.

## Abstract

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.