ODDF-Net: Multi-object segmentation in 3D retinal OCTA using optical density and disease features

Abstract

Automatic extraction of retinal structures, including Retinal Capillaries (RC), Retinal Arteries (RA), Retinal Veins (RV), and the Foveal Avascular Zone (FAZ), is crucial for the diagnosis and treatment of ocular diseases. This paper presents ODDF-Net, a segmentation network leveraging optical density and disease features, for the simultaneous 2D segmentation of RC, RA, RV, and FAZ in 3D Optical Coherence Tomography Angiography (OCTA). We introduce the concept of optical density to generate additional input images, enhancing the specificity for distinguishing arteries and veins. Our network employs a decoupled segmentation head to separate independent features of each object from shared features by focusing on object boundaries. Given the impact of ocular diseases on the morphology of retinal objects, we designed an auxiliary classification head and a cross-dimensional feature fusion module to model the relationship between various diseases and changes in retinal structures. Extensive experiments on two subsets of the OCTA-500 dataset demonstrate that ODDF-Net outperforms state-of-the-art methods, achieving mean intersection over union ratios of 88.17% and 82.80%. The source code is available at https://github.com/y8421036/ODDF-Net.