  SD-OCT is a non-invasive cross-sectional imaging modality used for diagnosis
of macular defects. Efficient detection and segmentation of the abnormalities
seen as biomarkers in OCT can help in analyzing the progression of the disease
and advising effective treatment for the associated disease. In this work, we
propose a fully automated Generalized Motion Pattern(GMP) based segmentation
method using a cascade of fully convolutional networks for detection and
segmentation of retinal fluids from SD-OCT scans. General methods for
segmentation depend on domain knowledge-based feature extraction, whereas we
propose a method based on Generalized Motion Pattern (GMP) which is derived by
inducing motion to an image to suppress the background.The proposed method is
parallelizable and handles inter-scanner variability efficiently. Our method
achieves a mean Dice score of 0.61,0.70 and 0.73 during segmentation and a mean
AUC of 0.85,0.84 and 0.87 during detection for the 3 types of fluids IRF, SRF
and PDE respectively.
