Cystoid macular edema segmentation of Optical Coherence Tomography images using fully convolutional neural networks and fully connected CRFs
This work addresses a domain-specific medical imaging problem for diagnosing diabetic macular edema, with incremental improvements over existing methods.
The paper tackled cystoid macular edema segmentation in retinal OCT images using a fully convolutional neural network and dense CRFs, achieving a Dice coefficient of 0.61±0.21, which outperformed the previous state-of-the-art of 0.51±0.34.
In this paper we present a new method for cystoid macular edema (CME) segmentation in retinal Optical Coherence Tomography (OCT) images, using a fully convolutional neural network (FCN) and a fully connected conditional random fields (dense CRFs). As a first step, the framework trains the FCN model to extract features from retinal layers in OCT images, which exhibit CME, and then segments CME regions using the trained model. Thereafter, dense CRFs are used to refine the segmentation according to the edema appearance. We have trained and tested the framework with OCT images from 10 patients with diabetic macular edema (DME). Our experimental results show that fluid and concrete macular edema areas were segmented with good adherence to boundaries. A segmentation accuracy of $0.61\pm 0.21$ (Dice coefficient) was achieved, with respect to the ground truth, which compares favourably with the previous state-of-the-art that used a kernel regression based method ($0.51\pm 0.34$). Our approach is versatile and we believe it can be easily adapted to detect other macular defects.