On the public citation graph

Micro-Vessel Image Segmentation Based on the AD-UNet Model

https://doi.org/10.1109/access.2019.2945556

0 signed · 78 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/mp.12953openalex-snapshot

Retinal vascular segmentation using superpixel‐based line operator and its application to vascular topology estimation

inferredcites →https://doi.org/10.1007/978-3-319-46723-8_17openalex-snapshot

Deep Retinal Image Understanding

inferredcites →https://doi.org/10.1007/s11263-017-1004-zopenalex-snapshot

Holistically-Nested Edge Detection

inferredcites →https://doi.org/10.1007/s11760-017-1114-7openalex-snapshot

Contrast normalization steps for increased sensitivity of a retinal image segmentation method

inferredcites →https://doi.org/10.1016/j.eswa.2018.06.034openalex-snapshot

Retinal vessel segmentation based on Fully Convolutional Neural Networks

inferredcites →https://doi.org/10.1088/1361-6560/ab0ef4openalex-snapshot

Automatic segmentation of levator hiatus from ultrasound images using U-net with dense connections

inferredcites →https://doi.org/10.1109/42.845178openalex-snapshot

Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response

inferredcites →https://doi.org/10.1109/access.2017.2786271openalex-snapshot

Drusen Segmentation From Retinal Images via Supervised Feature Learning

inferredcites →https://doi.org/10.1109/access.2019.2906344openalex-snapshot

A New Hybrid Algorithm for Retinal Vessels Segmentation on Fundus Images

inferredcites →https://doi.org/10.1109/access.2019.2922365openalex-snapshot

Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network

inferredcites →https://doi.org/10.1109/access.2019.2923006openalex-snapshot

Short-Term Photovoltaic Power Forecasting Based on Long Short Term Memory Neural Network and Attention Mechanism

inferredcites →https://doi.org/10.1109/access.2019.2935138openalex-snapshot

A Fundus Retinal Vessels Segmentation Scheme Based on the Improved Deep Learning U-Net Model

inferredcites →https://doi.org/10.1109/apccas.2008.4745977openalex-snapshot

Retinal vessel segmentation using histogram matching

inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot

Densely Connected Convolutional Networks

inferredcites →https://doi.org/10.1109/icdip.2009.70openalex-snapshot

Blood Vessel Enhancement and Segmentation Using Wavelet Transform

inferredcites →https://doi.org/10.1109/isbi.2016.7493362openalex-snapshot

Retinal vessel segmentation via deep learning network and fully-connected conditional random fields

inferredcites →https://doi.org/10.1109/ivcnz.2016.7804441openalex-snapshot

Automatic retinal vessel extraction algorithm based on contrast-sensitive schemes

inferredcites →https://doi.org/10.1109/tbme.2016.2535311openalex-snapshot

A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images

inferredcites →https://doi.org/10.1109/tmi.2004.825627openalex-snapshot

Ridge-Based Vessel Segmentation in Color Images of the Retina

inferredcites →https://doi.org/10.1109/tmi.2006.879955openalex-snapshot

Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction

inferredcites →https://doi.org/10.1109/tmi.2010.2064333openalex-snapshot

A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features

inferredcites →https://doi.org/10.1109/tmi.2015.2457891openalex-snapshot

A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images

inferredcites →https://doi.org/10.1109/tmi.2016.2546227openalex-snapshot

Segmenting Retinal Blood Vessels With Deep Neural Networks

inferredcites →https://doi.org/10.3390/e21020168openalex-snapshot

Dense U-net Based on Patch-Based Learning for Retinal Vessel Segmentation

inferredcites →https://doi.org/10.48550/arxiv.1706.09318openalex-snapshot

Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4022198openalex-extracted

Fine Root Image Processing Based on Deep Learning and Prior Knowledge

inferred← citeshttps://doi.org/10.1002/aisy.202501279openalex-snapshot

Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

inferred← citeshttps://doi.org/10.1002/esp.4888openalex-snapshot

Deep learning for dune pattern mapping with the AW3D30 global surface model

inferred← citeshttps://doi.org/10.1002/ima.70031openalex-snapshot

CBAM Attention Gate‐Based Lightweight Deep Neural Network Model for Improved Retinal Vessel Segmentation

inferred← citeshttps://doi.org/10.1002/jim4.15openalex-snapshot

Deep learning technology in vascular image segmentation and disease diagnosis

inferred← citeshttps://doi.org/10.1007/978-3-030-63799-6_3openalex-snapshot

Overlap Training to Mitigate Inconsistencies Caused by Image Tiling in CNNs

inferred← citeshttps://doi.org/10.1007/978-981-16-1244-2_32openalex-snapshot

VESDNet: Deep Vessel Segmentation (U) Network for the Early Diagnosis of Diabetic Retinopathy

inferred← citeshttps://doi.org/10.1007/978-981-96-9964-3_41openalex-snapshot

SATENet: Bridging Structural and Edge Semantics for Vessel Segmentation

inferred← citeshttps://doi.org/10.1007/978-981-99-0609-3_36openalex-snapshot

Comparative Analysis on Deep Learning Algorithms for Detecting Retinal Diseases Using OCT Images

inferred← citeshttps://doi.org/10.1007/s00542-020-05123-xopenalex-snapshot

Deep learning based real-time Industrial framework for rotten and fresh fruit detection using semantic segmentation

inferred← citeshttps://doi.org/10.1007/s10586-024-05085-zopenalex-snapshot

TransMA-Unet: transformer and multi-scale residual connection attention network for vessel segmentation

inferred← citeshttps://doi.org/10.1007/s11102-022-01255-7openalex-snapshot

Generating novel pituitary datasets from open-source imaging data and deep volumetric segmentation

inferred← citeshttps://doi.org/10.1007/s11831-023-09998-7openalex-snapshot

Automated Computationally Intelligent Methods for Ocular Vessel Segmentation and Disease Detection: A Review

inferred← citeshttps://doi.org/10.1007/s13721-025-00551-zopenalex-snapshot

Computationally efficient dilated residual networks for segmentation of major cerebral vessels in MRA

inferred← citeshttps://doi.org/10.1016/j.bspc.2022.104426openalex-snapshot

GDF-Net: A multi-task symmetrical network for retinal vessel segmentation

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.108857openalex-snapshot

CrossVesselNet: A one-shot retinal vessel segmentation Net

inferred← citeshttps://doi.org/10.1016/j.compag.2022.107397openalex-snapshot

Root hair image processing based on deep learning and prior knowledge

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.107770openalex-snapshot

An improved supervised and attention mechanism-based U-Net algorithm for retinal vessel segmentation

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108784openalex-snapshot

Channel prior convolutional attention for medical image segmentation

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.109789openalex-snapshot

Gabor-modulated depth separable convolution for retinal vessel segmentation in fundus images

inferred← citeshttps://doi.org/10.1016/j.fraope.2024.100182openalex-snapshot

Temporal dependency modeling for improved medical image segmentation: The R-UNet perspective

inferred← citeshttps://doi.org/10.1016/j.measurement.2022.112316openalex-snapshot

MAGF-Net: A multiscale attention-guided fusion network for retinal vessel segmentation

inferred← citeshttps://doi.org/10.1016/j.media.2022.102521openalex-snapshot

DARC: Deep adaptive regularized clustering for histopathological image classification

inferred← citeshttps://doi.org/10.1016/j.neucom.2023.126626openalex-snapshot

A comprehensive survey on segmentation techniques for retinal vessel segmentation

inferred← citeshttps://doi.org/10.1098/rsif.2021.0140openalex-snapshot

A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta

inferred← citeshttps://doi.org/10.1109/accai53970.2022.9752575openalex-snapshot

Comparative analysis on U-Net based Retinal Blood Vessel Segmentation

inferred← citeshttps://doi.org/10.1109/access.2021.3052870openalex-snapshot

Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions

inferred← citeshttps://doi.org/10.1109/access.2021.3068204openalex-snapshot

The Use of U-Net Lite and Extreme Gradient Boost (XGB) for Glaucoma Detection

inferred← citeshttps://doi.org/10.1109/access.2021.3102176openalex-snapshot

Retinal Vessel Segmentation Using Deep Learning: A Review

inferred← citeshttps://doi.org/10.1109/access.2022.3163247openalex-snapshot

A Survey of Deep Learning for Retinal Blood Vessel Segmentation Methods: Taxonomy, Trends, Challenges and Future Directions

inferred← citeshttps://doi.org/10.1109/access.2022.3163711openalex-snapshot

ELU-Net: An Efficient and Lightweight U-Net for Medical Image Segmentation

inferred← citeshttps://doi.org/10.1109/access.2023.3244197openalex-snapshot

Medical Image Segmentation Based on Transformer and HarDNet Structures

inferred← citeshttps://doi.org/10.1109/access.2023.3317176openalex-snapshot

Modified Depthwise Parallel Attention UNet for Retinal Vessel Segmentation

inferred← citeshttps://doi.org/10.1109/asiancon62057.2024.10838029openalex-snapshot

The U-Net Model and it’s Variants: A Revolution in Medical Image Segmentation

inferred← citeshttps://doi.org/10.1109/icbaie63306.2024.11117071openalex-snapshot

A Matching-Detection-Classification Cascaded Model for Small-Size Defect Identification on Transmission Lines

inferred← citeshttps://doi.org/10.1109/icece57408.2022.10088787openalex-snapshot

Retinal Blood Vessels Segmentation from Fundus Images Using U-Net

inferred← citeshttps://doi.org/10.1109/jbhi.2020.3028180openalex-snapshot

Accurate Retinal Vessel Segmentation in Color Fundus Images via Fully Attention-Based Networks

inferred← citeshttps://doi.org/10.1109/jsen.2022.3195365openalex-snapshot

SUR-Net: A Deep Network for Fish Detection and Segmentation With Limited Training Data

inferred← citeshttps://doi.org/10.1109/tai.2024.3351589openalex-snapshot

Soft Attention Mechanism Based Network to Extract Blood Vessels From Retinal Image Modality

inferred← citeshttps://doi.org/10.1109/wcsp55476.2022.10039399openalex-snapshot

STSANet: Retinal Vessel Segmentation via Spatial-Temporal and Self-Attention Encoding

inferred← citeshttps://doi.org/10.1155/2022/8030954openalex-snapshot

Retinal Blood Vessels and Optic Disc Segmentation Using U-Net

inferred← citeshttps://doi.org/10.17794/rgn.2024.2.openalex-snapshot

inferred← citeshttps://doi.org/10.17794/rgn.2024.2.1openalex-snapshot

DETERMINING ROCK FRAGMENT SIZE DISTRIBUTION USING A CONVOLUTIONAL NEURAL NETWORK

inferred← citeshttps://doi.org/10.2174/1573405618666220622154853openalex-snapshot

LFU-Net: A Lightweight U-Net with Full Skip Connections for Medical Image Segmentation

inferred← citeshttps://doi.org/10.22214/ijraset.2020.6352openalex-snapshot

Segmentation and Detection of Abnormalities in Retinal Blood Vessels

inferred← citeshttps://doi.org/10.32736/sisfokom.v12i2.1661openalex-snapshot

Counting Bacterial Colony and Reducing noise on Low-Quality Image Using Modified Perona-Malik Diffusion Filter with Sobel Mask Fractional Order

inferred← citeshttps://doi.org/10.3390/app11199119openalex-snapshot

EAR-Net: Efficient Atrous Residual Network for Semantic Segmentation of Street Scenes Based on Deep Learning

inferred← citeshttps://doi.org/10.3390/app13010198openalex-snapshot

Color Structured Light Stripe Edge Detection Method Based on Generative Adversarial Networks

inferred← citeshttps://doi.org/10.3390/diagnostics13030342openalex-snapshot

Retinal Blood-Vessel Extraction Using Weighted Kernel Fuzzy C-Means Clustering and Dilation-Based Functions

inferred← citeshttps://doi.org/10.3390/diagnostics13193152openalex-snapshot

Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model

inferred← citeshttps://doi.org/10.3390/e22080811openalex-snapshot

A Multi-Scale Feature Fusion Method Based on U-Net for Retinal Vessel Segmentation

inferred← citeshttps://doi.org/10.3390/s21093224openalex-snapshot

Quantification of Blood Flow Velocity in the Human Conjunctival Microvessels Using Deep Learning-Based Stabilization Algorithm

inferred← citeshttps://doi.org/10.3934/mbe.2023713openalex-snapshot

Spine MRI image segmentation method based on ASPP and U-Net network

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.