David Cunefare

TL;DR: A novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images is presented.

TL;DR: The usefulness of utilizing a segmentation step for improving the performance of sparsity based image reconstruction algorithms is demonstrated and the proposed SSR method for both denoising and interpolation of OCT images is demonstrated.

TL;DR: This work presents a convolutional neural network (CNN) based method for cone detection that learns features of interest directly from training data that resulted in comparable results to previous automatic cone segmentation methods which utilized ad hoc rules for different applications.

TL;DR: In this paper, a fully automatic adaptive filtering and local detection (AFLD) method was proposed for detecting cones in split detector AOSLO images. But, this method was not applied to split detector images.

TL;DR: A novel deep learning-based method called Deep OCT Atrophy Detection (DOCTAD) to automatically segment EZ defect areas by classifying 3-dimensional A-scan clusters as normal or defective, and is the first automatic segmentation method developed for EZ defects on OCT images of MacTel2.