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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
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IntroductionIntravenous Urography (IVU) and Retrograde Pyelography (RP) are important diagnostic imaging techniques used to evaluate the urinary system. These methods help identify structural abnormalities, obstructions, and functional issues in the kidneys, ureters, and bladder. Both procedures use iodine-based contrast media to enhance the visibility of urinary tract structures on X-ray images, though they differ in their methods and indications.1. Intravenous Urography (IVU)Intravenous...
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Artery vein classification in fundus images using serially connected U-Nets.

Robert Arnar Karlsson1, Sveinn Hakon Hardarson2

  • 1Faculty of Medicine at the University of Iceland, Sæmundargata 2, Reykjavík, 102, Iceland; Faculty of Electrical and Computer Engineering at the University of Iceland, Sæmundargata 2, Reykjavík, 102, Iceland.

Computer Methods and Programs in Biomedicine
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This study introduces a novel convolutional neural network for automated retinal vessel segmentation and artery-vein classification. The method achieves state-of-the-art performance, aiding in disease diagnosis.

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Area of Science:

  • Medical imaging analysis
  • Deep learning for ophthalmology
  • Computational pathology

Background:

  • Retinal vessels are crucial biomarkers for diagnosing systemic and ocular diseases.
  • Manual segmentation of retinal images is time-consuming and impractical.
  • Distinguishing between retinal arteries and veins is essential for accurate analysis.

Purpose of the Study:

  • To develop an automated method for simultaneous retinal vessel segmentation and artery-vein classification.
  • To evaluate the performance of a novel convolutional neural network architecture.
  • To compare the proposed method against state-of-the-art techniques and human experts.

Main Methods:

  • A convolutional neural network utilizing serially connected U-nets was proposed.
  • The network was trained and validated on public (DRIVE, HRF) and proprietary datasets.
  • Ablation experiments were conducted to assess the contribution of key network components.

Main Results:

  • Achieved F1 scores of 0.829 (DRIVE) and 0.814 (HRF) for vessel segmentation.
  • Reached F1 scores of 0.952 (DRIVE) and 0.966 (HRF) for artery-vein classification.
  • Outperformed state-of-the-art methods on the HRF dataset and human experts on the DRIVE dataset for classification.

Conclusions:

  • The proposed method demonstrates competitive and superior performance in vessel segmentation and classification.
  • The architecture effectively segments vasculature and classifies vessels, even in pathological images.
  • Serial connection of U-nets significantly enhances artery-vein classification accuracy.