A robust segmentation of retinal fluids from OCT images using MCFAR-net

Abstract

This paper presents a novel architecture to detect Macular Edema (ME) in Optical Coherence Tomography (OCT) images that occurs due to high fluid accumulation between the retinal layers of the eye. These excessive fluids have swollen the macular region and may result in visual impairments. To alleviate this problem in its early stage, the proposed model, Multiscale Context Enhancive Aggregation & Refinement Network (MCFAR-Net) is implemented to detect these fluids enabling the prevention of visual loss. This MCFAR-Net is incorporated with two Context Feature Enhancive Modules (CFEM). The upper module is initially trained using contrastive loss for extracting the most pertinent multiscale feature maps allowing to segment thick fluid regions from OCT images. Further, the lower module elevates the feature maps to segment the minute fluid regions using the outputs of upper CFEM along with input image. Finally, the output fluid probabilistic feature maps of both paths of the two modules are stacked together and fed as input to Feature Synthesizer (FS) Module. This module improves the true positive rate of the proposed algorithm and segments the fluid region more accurately. The performance of the proposed model is trained and evaluated using publicly available datasets like RETOUCH, OPTIMA, and DUKE datasets. This model outperformed existing state-of the-art algorithms by attaining an average dice coefficient of 95.63 % when tested on the RETOUCH dataset. The performance of proposed MCFAR-Net reduced the misclassification errors enabling to identify ME more precisely in its early stage allowing the expert doctor to provide immediate treatment to the patients.