# Fused Detection of Retinal Biomarkers in OCT Volumes

## TL;DR

This paper introduces a novel method combining CNNs and bidirectional LSTMs to automatically detect retinal biomarkers in OCT volumes, improving coherence and accuracy without requiring pixel-wise annotations.

## Contribution

The proposed approach fuses volume-wide information with biomarker prediction, avoiding pixel-wise annotations and enhancing detection consistency across OCT slices.

## Key findings

- Outperforms existing methods in biomarker detection accuracy.
- Ensures coherence of predictions across volume slices.
- Effective on a dataset of 416 OCT volumes.

## Abstract

Optical Coherence Tomography (OCT) is the primary imaging modality for detecting pathological biomarkers associated to retinal diseases such as Age-Related Macular Degeneration. In practice, clinical diagnosis and treatment strategies are closely linked to biomarkers visible in OCT volumes and the ability to identify these plays an important role in the development of ophthalmic pharmaceutical products. In this context, we present a method that automatically predicts the presence of biomarkers in OCT cross-sections by incorporating information from the entire volume. We do so by adding a bidirectional LSTM to fuse the outputs of a Convolutional Neural Network that predicts individual biomarkers. We thus avoid the need to use pixel-wise annotations to train our method, and instead provide fine-grained biomarker information regardless. On a dataset of 416 volumes, we show that our approach imposes coherence between biomarker predictions across volume slices and our predictions are superior to several existing approaches.