Retinal cysts segmentation in OCT via selective enhancement
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.
Biologically inspired automatic segmentation of candidate cyst regions in SD-OCT
3D imaging modalities are becoming increasingly popular and relevant in retinal imaging owing to their effectiveness in highlighting structures in sub-retinal layers. OCT is one such modality which has great importance in the context of analysis of cystoid structures in subretinal layers. Signal to noise ratio(SNR) of the images obtained from OCT is less and hence automated and accurate determination of cystoid structures from OCT is a challenging task.
We propose an automated method for detecting/segmenting cysts in 3D OCT volumes. Our method to accomplish this task is composed of different stages a preprocessing stage: to perform size normalizations of individual slices, A total variance denoising, enhancement using center surround; a candidate selection phase: segmentation of ILM and RPE layers to reduce the search region, MSER features for region selection; finally, false positive rejection stage. The proposed method is biologically inspired and fast aided by the domain knowledge about the cystoid structures. An ensemble learning methodRandom forests is learnt for classification of detected region into cyst region. The method achieves detection and segmentation in a unified setting. We believe the proposed approach with further improvements can be a promising starting point for more robust approach. This method is validated against the training set achieves a mean dice coefficient of 0.3893 with a standard deviation of 0.2987.