论文标题
一个用于视网膜血管分割的两条细节处理网络
A Two-Stream Meticulous Processing Network for Retinal Vessel Segmentation
论文作者
论文摘要
眼底的血管分割是眼科中的关键诊断能力，这项基本任务仍然存在各种挑战。早期方法表明，由于厚度不同的容器像素的不平衡，通常很难在薄容器和边界区域获得理想的分割性能。在本文中，我们提出了一个新颖的两流认证网络（MP-NET）来解决此问题。为了更加关注薄容器和边界区域，我们首先提出了一个有效的分层模型，将地面掩模自动分层为不同的厚度水平。然后，引入了一种新型的两流对抗网络，以使用平衡的损耗函数和集成操作使用分层结果，以实现更好的性能，尤其是在薄容器和边界区域中检测到。事实证明，我们的模型在驱动器，凝视和chase_db1数据集上的表现优于最先进的方法。
Vessel segmentation in fundus is a key diagnostic capability in ophthalmology, and there are various challenges remained in this essential task. Early approaches indicate that it is often difficult to obtain desirable segmentation performance on thin vessels and boundary areas due to the imbalance of vessel pixels with different thickness levels. In this paper, we propose a novel two-stream Meticulous-Processing Network (MP-Net) for tackling this problem. To pay more attention to the thin vessels and boundary areas, we firstly propose an efficient hierarchical model automatically stratifies the ground-truth masks into different thickness levels. Then a novel two-stream adversarial network is introduced to use the stratification results with a balanced loss function and an integration operation to achieve a better performance, especially in thin vessels and boundary areas detecting. Our model is proved to outperform state-of-the-art methods on DRIVE, STARE, and CHASE_DB1 datasets.
