Abstract:Optical coherence tomography angiography (OCTA) provides a detailed visualization of the vascular system to aid in the detection and diagnosis of ophthalmic disease. However, accurately extracting microvascular details from OCTA images remains a challenging task due to the limitations of pure convolutional networks. We propose a novel end-to-end transformer-based network architecture called TCU-Net for OCTA retinal vessel segmentation tasks. To address the loss of vascular features of convolutional operations,… Show more
“…Public datasets were also analyzed to extract embedded information and used to evaluate and/or compare algorithms. Shi et al [ 7 ] designed a novel transformer-based network architecture, called TCU-Net, for retinal vessel segmentation in optical coherence tomography angiography (OCTA) images. It addressed the limitations of traditional convolutional networks by introducing an efficient cross-fusion transformer module and a channel-wise cross-attention module.…”
“…Public datasets were also analyzed to extract embedded information and used to evaluate and/or compare algorithms. Shi et al [ 7 ] designed a novel transformer-based network architecture, called TCU-Net, for retinal vessel segmentation in optical coherence tomography angiography (OCTA) images. It addressed the limitations of traditional convolutional networks by introducing an efficient cross-fusion transformer module and a channel-wise cross-attention module.…”
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