Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper
Query: DUNet: A deformable network for retinal vessel segmentation
Segmentation of blood vessels from red-free and fluorescein retinal images
An approach to localize the retinal blood vessels using bit planes and centerline detection
Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks
Drishti-GS: Retinal image dataset for optic nerve head(ONH) segmentation
Deformable Convolutional Networks
Optic Disc Boundary and Vessel Origin Segmentation of Fundus Images
DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes
Automated detection of optic disc and blood vessel in retinal image using morphological, edge detection and feature extraction technique
Deformable ConvNets V2: More Deformable, Better Results
W-net: Bridged U-net for 2D Medical Image Segmentation.
Measurement of retinal vessel widths from fundus images based on 2-D modeling
Multi-scale morphological analysis for retinal vessel detection in wide-field fluorescein angiography
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
Retinal vessel extraction using first-order derivative of Gaussian and morphological processing
Fully Convolutional Networks and Generative Adversarial Networks Applied to Sclera Segmentation
Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images
Learning Deconvolution Network for Semantic Segmentation
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Machine learning algorithm for retinal image analysis
Deep retinal image understanding
Transformation Optics and the Geometry of Light
The impact of a set of interventions to reduce interruptions and distractions to nurses during medication administration
Sonography of the superomedial part of the spring ligament complex of the foot: a study of cadavers and asymptomatic volunteers
Gradient-Based Learning Applied to Document Recognition
free - form deformation of solid geometric models .