Sri Phani Krishna Karri

TL;DR: A new fully convolutional deep architecture, termed ReLayNet, is proposed for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans, validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods.

TL;DR: The approach fine-tunes a pre-trained convolutional neural network (CNN), GoogLeNet, to improve its prediction capability and identifies salient responses during prediction to understand learned filter characteristics.

TL;DR: In this article, a fully convolutional deep architecture, termed ReLayNet, is proposed for end-to-end segmentation of retinal layers and fluid masses in OCT scans.

TL;DR: The proposed algorithm simultaneously identifies individual layers and their corresponding edges, resulting in the computation of layer-specific edges in 1 second, which augment classical dynamic programming based segmentation under layer deformation, shadow artifacts noise, and without heuristics or prior knowledge.

TL;DR: A deep convolutional neural network (CNN) based solution is proposed, where images from random number of regions of the tissue section at multiple magnifications are analysed without any necessity of view correspondence across magnifications.