Label propagation and higher-order constraint-based segmentation of fluid-associated regions in retinal SD-OCT images

The segmentation of the fluid-associated region in the retina plays an important role in the treatment of retinal diseases, such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). The existing methods for the detection of the fluid region generally suffer from the limitation of the segmentation accuracy and the expensive time costs. To overcome these problems, in this paper, we propose an interactive segmentation method for the fluid-associated region in three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT) retinal imaging, in which only a few seeds in one SD-OCT slice are needed, after which the algorithm finishes the segmentation automatically. To improve the segmentation accuracy, the higher-order constraint is introduced into the conventional Markov random field (MRF) framework to impose the superpixel consistency. To maintain temporal coherence of the 3-D SD-OCT slices, the labeling information is propagated slice by slice via a proposed motion-estimation-based algorithm. The proposed higher-order-based energy function can be efficiently solved by the max flow algorithm on a specified graph with several auxiliary nodes. Experiments on 28 SD-OCT cubes demonstrate the competitiveness of the proposed method compared with the state-of-the-art methods.