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
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
The encoder utilizes the self-attention mechanism to capture long-range dependencies, while the decoder refines the feature maps preserving long-range information due to the global receptive fields of the graph convolution block.
An Accurate and Efficient Neural Network for OCTA Vessel Segmentation and a New Dataset
Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique that can reveal high-resolution retinal vessels.
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization
Driven by the latest trend towards self-supervised learning (SSL), the paradigm of "pretraining-then-finetuning" has been extensively explored to enhance the performance of clinical applications with limited annotations.
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations
To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data.
Block Attention and Switchable Normalization Based Deep Learning Framework for Segmentation of Retinal Vessels
The presence of high blood sugar levels damages blood vessels and causes an eye condition called diabetic retinopathy.
Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
For many years, convolutional neural networks dominated the field of computer vision, not least in the medical field, where problems such as image segmentation were addressed by such networks as the U-Net.
Retinal Image Restoration and Vessel Segmentation using Modified Cycle-CBAM and CBAM-UNet
The retinal vessel segmentation performance was compared with the ground-truth fundus images.
AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation
To address this issue, we propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG).
Exploring Generalizable Distillation for Efficient Medical Image Segmentation
Considering the domain-invariant representative vectors in MSAN, we propose two generalizable knowledge distillation schemes for cross-domain distillation, Dual Contrastive Graph Distillation (DCGD) and Domain-Invariant Cross Distillation (DICD).
OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation
In this work, we propose the application of the scribble-base weakly-supervised learning method to automate the pixel-level annotation.