This repository contains the implementation(partly) of a convolutional neural network used to segment retinal layers and fluid in eye OCT scans,termed ReLayNet. ReLayNet is based on U-Net architecture and is trained to optimize a joint loss function comprising ofweighted logistic regression and Dice overlap loss.
The Duke SD-OCT publicly available dataset for DME patients. The dataset consists of 110 annotated SD-OCT B-scan images of size 512 × 740 obtained from 10 patients suffering from DME (11 B-scans per patient). The 11 B-scans per patient were annotated centered at fovea and 5 frames on either side of the fovea (foveal slice and scans laterally acquired at ±2, ±5, ±10, ±15 and ± 20 from the foveal slice). These 110 B-scans are annotated for the retinal layers and fluid regions by two expert clinicians. The details of this datasets in this home-page.
The (a) is the segmentation of this code and (b) is the ground truth.
The Final dice_coef is 0.957
·Add Fluid segmentation
·Add the implementation of UNpooling with pooling indics