Retinal vessel segmentation via deep learning network and fully-connected conditional random fields
Explore this paper's citation graph
- Type
- article
- Published
- 2016-04-13
- Cited by
- 272
- References
- 18
- OpenAlex
- https://openalex.org/W2433259561
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:20852906
Keywords
Conditional random field, Artificial intelligence, Discriminative model, Computer science, CRFS
References
- Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images
- Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response
- Fully convolutional networks for semantic segmentation
- Segmentation of retinal blood vessels using the radial projection and semi-supervised approach
- A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features
- Retinal Imaging and Image Analysis
- Enhancement of blood vessels in digital fundus photographs via the application of multiscale line operators
- Segmentation of blood vessels from red-free and fluorescein retinal images
- An effective retinal blood vessel segmentation method using multi-scale line detection
- Ridge-based vessel segmentation in color images of the retina
- Caffe: Convolutional Architecture for Fast Feature Embedding
- Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- ImageNet classification with deep convolutional neural networks
- Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction
- Retinal Vessel Segmentation using Deep Neural Networks
- Holistically-Nested Edge Detection
- Deeply-Supervised Nets
- Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
- Holistically-Nested Edge Detection
Cited by
- Segmentation of carbon nanotube images through an artificial neural network
- A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation
- Multiscale Centerline Extraction Based on Regression and Projection onto the Set of Elongated Structures
- Spatial aggregation of holistically‐nested convolutional neural networks for automated pancreas localization and segmentation☆
- DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
- Recent Advancements in Retinal Vessel Segmentation
- A survey on deep learning in medical image analysis
- Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation
- Enhancing retinal vessel segmentation by color fusion
- Multi-stage segmentation of the fovea in retinal fundus images using fully Convolutional Neural Networks
- Generative Adversarial Learning for Reducing Manual Annotation in Semantic Segmentation on Large Scale Miscroscopy Images: Automated Vessel Segmentation in Retinal Fundus Image as Test Case
- Retinal blood vessel segmentation: methods and implementations
- Vascular tree tracking and bifurcation points detection in retinal images using a hierarchical probabilistic model
- A Review of Retinal Vessel Segmentation and Artery/Vein Classification
- Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks
- Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement
- Detección de vasos sanguíneos en retinografías mediante técnicas de procesado digital de imágenes
- A Skeletal Similarity Metric for Quality Evaluation of Retinal Vessel Segmentation
- Deep Deconvolutional Neural Network for Target Segmentation of Nasopharyngeal Cancer in Planning Computed Tomography Images
- Boosting Sensitivity of a Retinal Vessel Segmentation Algorithm with Convolutional Neural Network
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