Fused Detection of Retinal Biomarkers in OCT Volumes
This work addresses the challenge of clinical diagnosis and treatment strategies for retinal diseases like Age-Related Macular Degeneration, providing fine-grained biomarker information without pixel-wise annotations, though it is incremental as it builds on existing CNN and LSTM techniques.
The paper tackles the problem of automatically detecting retinal biomarkers in OCT volumes by incorporating information from the entire volume using a bidirectional LSTM fused with a CNN, avoiding the need for pixel-wise annotations. The method shows superior performance on a dataset of 416 volumes compared to existing approaches.
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.