LUNet：用于高分辨率眼底图像中细动脉和细静脉分割的深度学习方法
LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images
AI总结：
针对视网膜细动脉和细静脉的自动化分割问题，提出新型深度学习架构LUNet，通过双重膨胀卷积块和高分辨率长尾结构优化分割性能，并在多个测试集上显著优于现有算法。
摘要：
视网膜是人体中唯一可以使用数字眼底图像（DFI）等成像技术进行非侵入性血管观察的部位。视网膜微血管系统的空间分布可能随心血管疾病而改变，因此眼睛可被视为心脏的窗户。视网膜细动脉和细静脉（A/V）的计算机化分割对于自动化微血管分析至关重要。利用主动学习，我们创建了一个新的DFI数据集，其中包含由15名医学生执行并经眼科医生审查的240个众包手动A/V分割结果，并开发了LUNet，这是一种用于高分辨率A/V分割的新型深度学习架构。LUNet架构包含一个双重膨胀卷积块，旨在增强模型的感受野并减少其参数量。此外，LUNet具有在高分辨率下运行的长尾结构以细化分割结果。自定义损失函数强调了血管的连续性。结果表明，LUNet在本地测试集以及四个模拟不同种族、合并症和标注者分布偏移的外部测试集上，均显著优于两种最先进的分割算法。我们将新创建的数据集开放获取（发表时）。
英文摘要：
The retina is the only part of the human body in which blood vessels can be accessed non-invasively using imaging techniques such as digital fundus images (DFI). The spatial distribution of the retinal microvasculature may change with cardiovascular diseases and thus the eyes may be regarded as a window to our hearts. Computerized segmentation of the retinal arterioles and venules (A/V) is essential for automated microvasculature analysis. Using active learning, we created a new DFI dataset containing 240 crowd-sourced manual A/V segmentations performed by fifteen medical students and reviewed by an ophthalmologist, and developed LUNet, a novel deep learning architecture for high resolution A/V segmentation. LUNet architecture includes a double dilated convolutional block that aims to enhance the receptive field of the model and reduce its parameter count. Furthermore, LUNet has a long tail that operates at high resolution to refine the segmentation. The custom loss function emphasizes the continuity of the blood vessels. LUNet is shown to significantly outperform two state-of-the-art segmentation algorithms on the local test set as well as on four external test sets simulating distribution shifts across ethnicity, comorbidities, and annotators. We make the newly created dataset open access (upon publication).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
