论文标题
深度OCT血管造影图像生成运动伪影抑制
Deep OCT Angiography Image Generation for Motion Artifact Suppression
论文作者
论文摘要
在获得光学相干断层扫描（OCT）期间，眼睛运动，眨眼和其他运动可能会导致伪影，当处理到OCT血管造影（八八片）图像时。受影响的扫描出现为高强度（白色）或缺失（黑色）区域，导致信息丢失。这项研究的目的是使用深层生成模型填补这些空白，以依靠单个完整的OCT扫描，以将OCT到OCTA图像翻译。因此，对U-NET进行了训练，可以从OCT斑块中提取血管造影信息。在推断时，检测算法根据周围环境发现了八块离群值扫描，然后由训练有素的网络取代。我们表明，生成模型可以增强缺失的扫描。然后，增强体积可用于3-D分割或增加诊断值。
Eye movements, blinking and other motion during the acquisition of optical coherence tomography (OCT) can lead to artifacts, when processed to OCT angiography (OCTA) images. Affected scans emerge as high intensity (white) or missing (black) regions, resulting in lost information. The aim of this research is to fill these gaps using a deep generative model for OCT to OCTA image translation relying on a single intact OCT scan. Therefore, a U-Net is trained to extract the angiographic information from OCT patches. At inference, a detection algorithm finds outlier OCTA scans based on their surroundings, which are then replaced by the trained network. We show that generative models can augment the missing scans. The augmented volumes could then be used for 3-D segmentation or increase the diagnostic value.
