Recurrent Task Specialization Network for Segmentation-aided Vascular Landmarks Detection in Retinal Images

Abstract

[Abstract]: The detection of vessel crossings and bifurcations in eye fundus images plays an important role in numerous applications, including the diagnosis of ophthalmic and systemic diseases, biometric authentication, and retinal image registration. Nowadays, deep neural networks are successfully used for the detection of these vascular landmarks. However, existing approaches could be limited by the lack of understanding of the retinal anatomy and the intricate retinal vasculature. In this context, we propose Recurrent Task Specialization, a novel approach that performs a recurrent forward process with two forward passes through the same network, each of them specialized in a different task. We apply the proposed approach to the detection of vessel crossings and bifurcations in the retina via heatmap regression, using the segmentation of the retinal vasculature as the auxiliary task. To validate our proposal, we perform comparative experiments on two public datasets, including common alternatives to leverage auxiliary tasks, such as standard multi-task learning and transfer learning. The proposed approach outperforms existing alternatives and achieves the best results in the state-of-the-art for the detection of vessel crossings and bifurcations in retinal images. In this regard, our experiments demonstrate the potential of the proposed approach to improve the performance of deep neural networks in applications where adequate auxiliary tasks can be constructed.