Classification of drusen positions in optical coherence tomography data from patients with age-related macular degeneration

Quantitative analysis of optical coherence tomography volumes is an important tool for both clinicians and researchers. Until now, most work has focused on segmentation of the intraretinal cell layers, but the segmentation of pathological datasets remains challenging. We propose the application of random forest to detect the locations of drusen in the retinal pigment epithelium. This is an important step for further analysis of optical coherence tomography data, for segmentation or otherwise. The presented combination of Bruch's Membrane segmentation with subsequent sampling around the retinal pigment epithelium is a way to quickly compute discriminative features for classification. The proposed method achieves an AUC of 0.94 on our test set, while keeping the computational complexity at a minimum. This makes a clinical setup feasible and provides a tool for clinicians and researchers to quantitatively measure disease progession.