Segmentation of retinal cysts from Optical Coherence Tomography volumes via selective enhancement

TL;DR

This paper introduces a biologically inspired, CNN-based method for 3D OCT cyst segmentation that enhances cyst localization through motion-induced selective enhancement, achieving state-of-the-art accuracy across multiple datasets.

Contribution

A novel cyst localization technique using motion-induced enhancement combined with CNNs for improved 3D OCT segmentation accuracy.

Findings

Achieved a mean Dice Coefficient of 0.71 on the MICCAI 2015 challenge dataset.

Demonstrated robustness with mean DC of 0.69 and 0.79 on DME and private datasets.

Outperformed all benchmark methods in cyst segmentation accuracy.

Abstract

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.