Rethinking Retinal Landmark Localization as Pose Estimation: Naïve Single Stacked Network for Optic Disk and Fovea Detection

Abstract

Automatic detection of optic disk and fovea, the two fundamental biological landmarks of the retinal system, is crucial to track the disease progression in a diabetic patient. Recent advances in this direction were mostly limited to applying CNN based networks to aggressively extract visual geometric features. In a departure from that practice, we put forward the notion of treating the landmark detection problem in human eye scans as a pose estimation problem owing to the anatomical geometrical relationship between optic disk and fovea. In this regard, we present Naive Single Stacked Hourglass (NSSH) network which learns the spatial orientation and pixel intensity contrast between optic disk and fovea to accurately pinpoint their locations. NSSH network significantly reduces the mean squared loss, thus outperforming all previously known techniques and establishing a state of the art in both optic disk and fovea localization tasks.