Retinal Vessel Segmentation
45 papers with code • 8 benchmarks • 6 datasets
Retinal vessel segmentation is the task of segmenting vessels in retina imagery.
( Image credit: LadderNet )
Libraries
Use these libraries to find Retinal Vessel Segmentation models and implementationsLatest papers with no code
Enhancing Retinal Vascular Structure Segmentation in Images With a Novel Design Two-Path Interactive Fusion Module Model
Precision in identifying and differentiating micro and macro blood vessels in the retina is crucial for the diagnosis of retinal diseases, although it poses a significant challenge.
A publicly available vessel segmentation algorithm for SLO images
Background and Objective: Infra-red scanning laser ophthalmoscope (IRSLO) images are akin to colour fundus photographs in displaying the posterior pole and retinal vasculature fine detail.
FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures
The proposed solution has been evaluated using the DRIVE, CHASE-DB1, and STARE datasets, and competitive results are obtained when compared with previous studies.
FRS-Nets: Fourier Parameterized Rotation and Scale Equivariant Networks for Retinal Vessel Segmentation
To embed more equivariance into CNNs and achieve the accuracy requirement for retinal vessel segmentation, we construct a novel convolution operator (FRS-Conv), which is Fourier parameterized and equivariant to rotation and scaling.
Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks
However, fixed values of atrous rates are used for the ASPP module, which restricts the size of its field of view.
RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation
Retinal vessel segmentation is generally grounded in image-based datasets collected with bench-top devices.
VesselMorph: Domain-Generalized Retinal Vessel Segmentation via Shape-Aware Representation
We map the intensity image and the tensor field to a latent space for feature extraction.
Deep Angiogram: Trivializing Retinal Vessel Segmentation
The generalizability of the synthetic network is improved by the contrastive loss that makes the model less sensitive to variations of image contrast and noisy features.
Overview of Deep Learning Methods for Retinal Vessel Segmentation
Methods for automated retinal vessel segmentation play an important role in the treatment and diagnosis of many eye and systemic diseases.
Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-aware Network
Second, we extract the uncertainty under different scales and propose the multi-scale uncertainty-aware (MSUA) fusion module to integrate structure contexts from hierarchical predictions, strengthening the final prediction.