A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation
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- Type
- article
- Published
- 2016-11-07
- Cited by
- 241
- References
- 21
- Access
- Open access
- OpenAlex
- https://openalex.org/W2556022279
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:335714
Keywords
Convolutional neural network, Segmentation, Computer science, Artificial intelligence, Pattern recognition (psychology)
References
- Segmenting Retinal Blood Vessels With Deep Neural Networks
- Clinical Ophthalmology: A Systematic Approach
- A New Approach to Segment Both Main and Peripheral Retinal Vessels Based on Gray-Voting and Gaussian Mixture Model
- Retinal vessel extraction using Lattice Neural Networks with dendritic processing
- Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification
- Comparative study of retinal vessel segmentation methods on a new publicly available database
- Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation
- Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images
- Retinal vessel extraction by matched filter with first-order derivative of Gaussian
- Ridge-based vessel segmentation in color images of the retina
- Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification
- A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering
- Vessel extraction from non-fluorescein fundus images using orientation-aware detector
- Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images
- A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images
- Retinal vessel segmentation via deep learning network and fully-connected conditional random fields
- Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography
- Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques
- Retinal Vessel Extraction Using First-Order Derivative of Gaussian and Morphological Processing
- Contrast Limited Adaptive Histogram Equalization
Cited by
- A Labeling-Free Approach to Supervising Deep Neural Networks for Retinal Blood Vessel Segmentation
- Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics
- Retinal blood vessel segmentation based on the Gaussian matched filter and U-net
- Retinal Vessels Segmentation Techniques and Algorithms: A Survey
- Automatic Coronary Artery Segmentation in X-ray Angiograms by Multiple Convolutional Neural Networks
- Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function
- Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation
- Greedy Graph Searching for Vascular Tracking in Angiographic Image Sequences
- Binary Filter for Fast Vessel Pattern Extraction
- Retinal blood vessels semantic segmentation method based on modified U-Net
- Using Fully Convolutional Networks For Semantic Segmentation of Diabetic Retinopathy Lesions in Retinal Images
- Toward Improving Safety in Neurosurgery with an Active Handheld Instrument
- Multichannel Fully Convolutional Network for Coronary Artery Segmentation in X-Ray Angiograms
- Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach
- Freight car target detection in a complex background based on convolutional neural networks
- Retinal Blood Vessel Segmentation Based on Multi-Scale Deep Learning
- A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation
- DUNet: A deformable network for retinal vessel segmentation
- A novel retinal vessel detection approach based on multiple deep convolution neural networks
- Deep Learning based Computer-Aided Diagnosis Systems for Diabetic Retinopathy: A Survey
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