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Updated: Jan 23, 2026

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Annotation-free 3D reconstruction and quantification of retinal microvasculature by RADAR.

Hao Zhang1,2, Xindi Liu3, Jiayi Wu4

  • 1State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Xi'an, China.

NPJ Digital Medicine
|January 21, 2026
PubMed
Summary
This summary is machine-generated.

RADAR, an annotation-free framework, accurately maps retinal microvasculature in 3D using optical coherence tomography angiography. This tool aids in early detection and monitoring of vascular diseases by analyzing vessel changes.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computational Biology

Background:

  • Accurate 3D retinal microvasculature mapping is crucial for assessing systemic vascular health.
  • Current methods, including manual annotation and deep learning, face limitations in generalizability and require extensive training.
  • There is a need for automated, robust, and generalizable tools for retinal vascular analysis.

Purpose of the Study:

  • To introduce RADAR, an annotation-free computational framework for 3D segmentation and quantification of optical coherence tomography angiography (OCTA) data.
  • To enable precise reconstruction and analysis of complex retinal microvascular networks without manual labeling.
  • To validate RADAR's performance in healthy individuals and patients with diabetic retinopathy.

Main Methods:

  • Development of RADAR, an annotation-free computational framework integrating adaptive physics-aware denoising and topology-preserving centerline extraction.
  • Application of the framework to segment and quantify 3D retinal microvasculature from OCTA data.
  • Validation in healthy subjects and patients with early-stage diabetic retinopathy, comparing with standard segmentation tools.

Main Results:

  • RADAR successfully performed annotation-free 3D segmentation and quantification of retinal microvasculature.
  • The framework outperformed standard segmentation tools, resolving layer-specific alterations.
  • Analysis revealed compensatory remodeling and increased tortuosity in diabetic retinopathy patients' retinal vasculature.
  • Volumetric biomarkers such as vessel length and branching complexity were precisely extracted.

Conclusions:

  • RADAR provides a scalable and accurate computational tool for 3D retinal microvasculature analysis.
  • The framework facilitates early detection and longitudinal assessment of ocular and systemic vascular diseases.
  • RADAR's ability to resolve fine morphological details offers new insights into vascular pathologies like diabetic retinopathy.