  Many eye diseases like Diabetic Macular Edema (DME), Age-related Macular
Degeneration (AMD), and Glaucoma manifest in the retina, can cause irreversible
blindness or severely impair the central version. The Optical Coherence
Tomography (OCT), a 3D scan of the retina with high qualitative information
about the retinal morphology, can be used to diagnose and monitor changes in
the retinal anatomy. Many Deep Learning (DL) methods have shared the success of
developing an automated tool to monitor pathological changes in the retina.
However, the success of these methods depend mainly on large datasets. To
address the challenge from very small and limited datasets, we proposed a DL
architecture termed CoNet (Coherent Network) for joint segmentation of layers
and fluids in retinal OCT images on very small datasets (less than a hundred
training samples). The proposed model was evaluated on the publicly available
Duke DME dataset consisting of 110 B-Scans from 10 patients suffering from DME.
Experimental results show that the proposed model outperformed both the human
experts' annotation and the current state-of-the-art architectures by a clear
margin with a mean Dice Score of 88% when trained on 55 images without any data
augmentation.
