ArticleIEEE transactions on bio-medical engineering2026
Freqformer: Frequency-Domain Transformer for 3-D Reconstruction and Quantification of Human Retinal Vasculature.
Article in IEEE transactions on bio-medical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A Three-dimensional Analytical Framework for Retinal Microvasculature Reveals Layer-associated Vulnerability in Development and Neovascular Remodeling.bioRxiv : the preprint server for biology · 2026Article
- Fourier Transform Multiple Instance Learning for whole slide image classification.Journal of medical imaging (Bellingham, Wash.) · 2025Article
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5 authors.
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Abstract
objectiveTo achieve accurate 3-D reconstruction and quantitative analysis of human retinal vasculature from a single optical coherence tomography angiography (OCTA) scan.
methodsWe introduce Freqformer, a novel Transformer-based model featuring a dual-branch architecture that integrates a Transformer layer for capturing global spatial context with a complex-valued frequency-domain module designed for adaptive frequency enhancement. Freqformer was trained using single depth-plane OCTA images, utilizing volumetrically merged OCTA as ground truth. Performance was evaluated quantitatively through 2-D and 3-D image metrics. 2-D networks and their 3-D counterparts were compared to assess the differences between enhancing volume slice by slice and enhancing it by 3-D patches. Furthermore, 3-D quantitative vascular metrics were conducted to quantify human retinal vasculature.
resultsFreqformer substantially outperformed existing convolutional neural networks and Transformer-based methods, achieving superior image metrics. Importantly, the enhanced OCTA volumes show strong correlation with the merged volumes on vascular segment count, density, length, and flow index, further underscoring its reliability for quantitative vascular analysis. 3-D counterparts did not yield additional gains in image metrics or downstream 3-D vascular quantification but incurred nearly an order-of-magnitude longer inference time, supporting our 2-D slice-wise enhancement strategy. Additionally, Freqformer showed excellent generalization capability on larger field-of-view scans, surpassing the quality of conventional volumetric merging methods.
conclusionFreqformer reliably generates high-definition 3-D retinal microvasculature from single-scan OCTA, enabling precise vascular quantification comparable to standard volumetric merging methods. SIGNIFICANCE: Freqformer has promising clinical applications in early detection, detailed microvasculature analysis, and monitoring of retinal vascular diseases through advanced 3-D quantitative assessments.
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