论文标题
SPNET：基于共享解码器和金字塔样损失的新型深层神经网络，用于视网膜血管分割
SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss
论文作者
论文摘要
视网膜血管图像的分割对于视网膜病的诊断至关重要。最近，卷积神经网络表现出了提取血管结构的显着能力。但是，由于厚度不一致和边界模糊的毛细血管和视网膜血管边缘的精制分割仍然具有挑战性。在本文中，我们提出了一个基于共享解码器和类似金字塔的损失（SPNET）的新型深层神经网络，以解决上述问题。具体而言，我们引入了一种解码器共享机制来捕获多尺度语义信息，其中在其中通过一系列重量共享解码器模块来解码各种尺度的特征图。此外，为了加强对血管的毛细血管和边缘的表征，我们定义了一个残留的金字塔结构，该结构在解码阶段分解了空间信息。金字塔样损耗函数旨在逐步补偿可能的分割误差。公共基准的实验结果表明，该提出的方法的表现优于骨干网络和最先进的方法，尤其是在毛细血管和血管轮廓的区域。此外，跨数据集的性能验证了SPNET表现出更强的概括能力。
Segmentation of retinal vessel images is critical to the diagnosis of retinopathy. Recently, convolutional neural networks have shown significant ability to extract the blood vessel structure. However, it remains challenging to refined segmentation for the capillaries and the edges of retinal vessels due to thickness inconsistencies and blurry boundaries. In this paper, we propose a novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss (SPNet) to address the above problems. Specifically, we introduce a decoder-sharing mechanism to capture multi-scale semantic information, where feature maps at diverse scales are decoded through a sequence of weight-sharing decoder modules. Also, to strengthen characterization on the capillaries and the edges of blood vessels, we define a residual pyramid architecture which decomposes the spatial information in the decoding phase. A pyramid-like loss function is designed to compensate possible segmentation errors progressively. Experimental results on public benchmarks show that the proposed method outperforms the backbone network and the state-of-the-art methods, especially in the regions of the capillaries and the vessel contours. In addition, performances on cross-datasets verify that SPNet shows stronger generalization ability.
