Abstract
As a new imaging modality, optical coherence tomography angiography (OCTA) can fully explore the characteristics of retinal blood flow. Considering the inconvenience of acquiring OCTA images and inevitable mechanical artifacts, we introduce deep learning to generate OCTA from OCT. In this paper, we propose a texture-guided down- and up-sampling model based on U-Net for OCT-to-OCTA generation. A novel texture-guided sampling block is proposed by combining the extracted texture features and content-adaptive convolutions. The corresponding down-sampling and up-sampling operations would preserve more textural details during the convolutions and deconvolutions, respectively. Then a deeply-supervised texture-guided U-Net is constructed by substituting the traditional convolution with the texture-guided sampling blocks. Moreover, the image Euclidean distance is utilized to construct the loss function, which is more robust to noise and could explore more useful similarities involved in OCT and OCTA images. The dataset containing paired OCT and OCTA images from 489 eyes diagnosed with various retinal diseases is used to evaluate the performance of the proposed network. The results based on cross validation experiments demonstrate the stability and superior performances of the proposed model comparing with state-of-the-art semantic segmentation models and GANs.
The authors declare no conflicts of interest. This work was supported in part by National Science Foundation of China under Grants No. 62072241, in part by Natural Science Foundation of Jiangsu Province under Grant No. BK20180069, in part by Six talent peaks project in Jiangsu Province under Grant No. SWYY-056, and in part by National Institutes of Health Grant No. P30-EY026877.
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Zhang, Z., Ji, Z., Chen, Q., Yuan, S., Fan, W. (2021). Texture-Guided U-Net for OCT-to-OCTA Generation. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13022. Springer, Cham. https://doi.org/10.1007/978-3-030-88013-2_4
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