Deep Neural Network Regression for Automated Retinal Layer Segmentation in Optical Coherence Tomography Images.Segmenting the retinal layers in optical coherence tomography (OCT) images helps to quantify the layer information in early diagnosis of retinal diseases, which are the main cause of permanent blindness.  The proposed deep neural network regression takes the intensity, gradient, and adaptive normalized intensity score (ANIS) of an image segment as features for learning, and then predicts the corresponding retinal boundary pixel. 
Deep Neural Network Regression
Deep Neural Network Regression for Automated Retinal Layer Segmentation in Optical Coherence Tomography Images.Segmenting the retinal layers in optical coherence tomography (OCT) images helps to quantify the layer information in early diagnosis of retinal diseases, which are the main cause of permanent blindness. 
Optical Coherence Tomography Images
The proposed deep neural network regression takes the intensity, gradient, and adaptive normalized intensity score (ANIS) of an image segment as features for learning, and then predicts the corresponding retinal boundary pixel.  In addition, assisted by ANIS, the method operates robustly on OCT images containing intensity variances, low-contrast regions, speckle noise, and blood vessels, yet remains accurate and time-efficient. 
ANIS
Further, the Dice similarity coefficient used for assessing accuracy gave a computed value of approximately 0.966. 
Dice similarity coefficient