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Retinal Vessels

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SCANet: A Unified Semi-Supervised Learning Framework for Vessel Segmentation.

IEEE transactions on medical imaging
Automatic subcutaneous vessel imaging with near-infrared (NIR) optical apparatus can promote the accuracy of locating blood vessels, thus significantly contributing to clinical venipuncture research. Though deep learning models have achieved remarkab...

SegR-Net: A deep learning framework with multi-scale feature fusion for robust retinal vessel segmentation.

Computers in biology and medicine
Retinal vessel segmentation is an important task in medical image analysis and has a variety of applications in the diagnosis and treatment of retinal diseases. In this paper, we propose SegR-Net, a deep learning framework for robust retinal vessel s...

Retinal vessel segmentation via a Multi-resolution Contextual Network and adversarial learning.

Neural networks : the official journal of the International Neural Network Society
Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness. Accurate retinal vessel segmentation plays an important role in disease progression and diagnosis of such vision-threatening diseases. To this end,...

Comparing Common Retinal Vessel Caliber Measurement Software with an Automatic Deep Learning System.

Current eye research
PURPOSE: To compare the Retina-based Microvascular Health Assessment System (RMHAS) with Integrative Vessel Analysis (IVAN) for retinal vessel caliber measurement.

BCU-Net: Bridging ConvNeXt and U-Net for medical image segmentation.

Computers in biology and medicine
Medical image segmentation enables doctors to observe lesion regions better and make accurate diagnostic decisions. Single-branch models such as U-Net have achieved great progress in this field. However, the complementary local and global pathologica...

TUnet-LBF: Retinal fundus image fine segmentation model based on transformer Unet network and LBF.

Computers in biology and medicine
Segmentation of retinal fundus images is a crucial part of medical diagnosis. Automatic extraction of blood vessels in low-quality retinal images remains a challenging problem. In this paper, we propose a novel two-stage model combining Transformer U...

Sex determination using color fundus parameters in older adults of Kumejima population study.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: Deep learning artificial intelligence can determine the sex using only fundus photographs. However, the factors used by deep learning to determine the sex are not visible. Therefore, the purpose of the study was to determine whether the sex ...

Automatic vessel crossing and bifurcation detection based on multi-attention network vessel segmentation and directed graph search.

Computers in biology and medicine
Analysis of the vascular tree is the basic premise to automatically diagnose retinal biomarkers associated with ophthalmic and systemic diseases, among which accurate identification of intersection and bifurcation points is quite challenging but impo...

Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images.

Interdisciplinary sciences, computational life sciences
Accurate segregation of retinal blood vessels network plays a crucial role in clinical assessments, treatments, and rehabilitation process. Owing to the presence of acquisition and instrumentation anomalies, precise tracking of vessels network is cha...

MC-UNet: Multimodule Concatenation Based on U-Shape Network for Retinal Blood Vessels Segmentation.

Computational intelligence and neuroscience
Accurate retinal blood vessels segmentation is an important step in the clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and un...