Vessel-Promoted OCT to OCTA Image Translation by Heuristic Contextual Constraints

• URL: http://arxiv.org/abs/2303.06807v2
• Date: Wed, 21 Aug 2024 15:25:51 GMT
• Title: Vessel-Promoted OCT to OCTA Image Translation by Heuristic Contextual Constraints
• Authors: Shuhan Li, Dong Zhang, Xiaomeng Li, Chubin Ou, Lin An, Yanwu Xu, Kwang-Ting Cheng,
• Abstract summary: We introduce a novel method called TransPro to translate readily available 3D Optical Coherence Tomography images into 3D OCTA images. Our TransPro method is primarily driven by two novel ideas that have been overlooked by prior work. Experimental results on two datasets demonstrate that our TransPro outperforms state-of-the-art approaches.
• Score: 28.715207556565638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical Coherence Tomography Angiography (OCTA) is a crucial tool in the clinical screening of retinal diseases, allowing for accurate 3D imaging of blood vessels through non-invasive scanning. However, the hardware-based approach for acquiring OCTA images presents challenges due to the need for specialized sensors and expensive devices. In this paper, we introduce a novel method called TransPro, which can translate the readily available 3D Optical Coherence Tomography (OCT) images into 3D OCTA images without requiring any additional hardware modifications. Our TransPro method is primarily driven by two novel ideas that have been overlooked by prior work. The first idea is derived from a critical observation that the OCTA projection map is generated by averaging pixel values from its corresponding B-scans along the Z-axis. Hence, we introduce a hybrid architecture incorporating a 3D adversarial generative network and a novel Heuristic Contextual Guidance (HCG) module, which effectively maintains the consistency of the generated OCTA images between 3D volumes and projection maps. The second idea is to improve the vessel quality in the translated OCTA projection maps. As a result, we propose a novel Vessel Promoted Guidance (VPG) module to enhance the attention of network on retinal vessels. Experimental results on two datasets demonstrate that our TransPro outperforms state-of-the-art approaches, with relative improvements around 11.4% in MAE, 2.7% in PSNR, 2% in SSIM, 40% in VDE, and 9.1% in VDC compared to the baseline method. The code is available at: https://github.com/ustlsh/TransPro.

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