  Automated and accurate segmentation of cystoid structures in Optical
Coherence Tomography (OCT) is of interest in the early detection of retinal
diseases. It is, however, a challenging task. We propose a novel method for
localizing cysts in 3D OCT volumes. The proposed work is biologically inspired
and based on selective enhancement of the cysts, by inducing motion to a given
OCT slice. A Convolutional Neural Network (CNN) is designed to learn a mapping
function that combines the result of multiple such motions to produce a
probability map for cyst locations in a given slice. The final segmentation of
cysts is obtained via simple clustering of the detected cyst locations. The
proposed method is evaluated on two public datasets and one private dataset.
The public datasets include the one released for the OPTIMA Cyst segmentation
challenge (OCSC) in MICCAI 2015 and the DME dataset. After training on the OCSC
train set, the method achieves a mean Dice Coefficient (DC) of 0.71 on the OCSC
test set. The robustness of the algorithm was examined by cross-validation on
the DME and AEI (private) datasets and a mean DC values obtained were 0.69 and
0.79, respectively. Overall, the proposed system outperforms all benchmarks.
These results underscore the strengths of the proposed method in handling
variations in both data acquisition protocols and scanners.
