Proceedings

FlowOCT: Wavelet Flow Matching for OCT-to-OCTA Translation

Ze Xiong, Dehui Qiu, Liguo Deng, Longfei Zhou, Zhetao Xu, Fa Zhang, Xiaohua Wan

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 1911-1919.

https://doi.org/10.24963/ijcai.2026/213

Keywords

  • Computer Vision: Multimodal learning
  • Computer Vision: Low-level Vision
  • Computer Vision: Biomedical image analysis

Abstract

Retinal angiography provides critical vascular information, yet optical coherence tomography angiography (OCTA) acquisition remains slower and less accessible than conventional structural OCT. A key open question is: how angiographic signals can be recovered? To address the issues of artifacts and structural blurring in existing generative methods, we propose a wavelet flow matching-based model, FlowOCT. This method operates directly on OCT wavelet coefficients, learning frequency-specific velocity fields to map them to the OCTA wavelet distribution. To ensure anatomical accuracy of the vasculature, we introduce constraints such as depth-wise contrastive alignment and two-dimensional projection consistency. Given the sparse nature of vascular signals in OCTA data, a structure-focused loss function and corresponding evaluation metrics are designed. Experiment results show that FlowOCT outperforms existing methods in terms of both image quality and perceptual similarity. Downstream diagnostic tasks further validate its superior authenticity and generalization capability. Code is available at https://github.com/cnu-medilab/FlowOCT.

Ze Xiong, Dehui Qiu, Liguo Deng, Longfei Zhou, Zhetao Xu, Fa Zhang, and Xiaohua Wan. 2026. FlowOCT: Wavelet Flow Matching for OCT-to-OCTA Translation. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 1911–1919. https://doi.org/10.24963/ijcai.2026/213
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