本文的第一作者为博士生王搏，题目为“CSU-Net: A Context Spatial U-Net for Accurate Blood Vessel Segmentation in Fundus Images”，被IEEE Journal of Biomedical and Health Informatics 录用。

以下为文章摘要：
Blood vessel segmentation in fundus images is a critical procedure in the diagnosis of ophthalmic diseases. Recent deep learning methods achieve high accuracy in vessel segmentation but still face the challenge to segment the microvascular and detect the vessel boundary. This is due to the fact that common Convolutional Neural Networks (CNN) are unable to preserve rich spatial information and a large receptive field simultaneously. Besides, CNN models for vessel segmentation usually are trained by equal pixel level cross-entropy loss, which tend to miss fine vessel structures. In this paper, we propose a novel Context Spatial U-Net (CSU-Net) for blood vessel segmentation. Compared with the other U-Net based models, we design a two-channel encoder: a context channel with multi-scale convolution to capture more receptive field and a spatial channel with large kernel to retain spatial information. Also, to combine and strengthen the features extracted from two paths, we introduce a feature fusion module (FFM) and an attention skip module (ASM). Furthermore, we propose a structure loss, which adds a spatial weight to cross-entropy loss and guide the network to focus more on the thin vessels and boundaries. We evaluated this model on three public datasets: DRIVE, CHASE-DB1 and STARE. The results show that the CSU-Net achieves higher segmentation accuracy than the current state-of-the-art methods.

中文摘要参考：
眼底图像中的血管分割是眼科疾病诊断的关键步骤。 最新的深度学习方法在血管分割中实现了高精度，但是仍然面临着分割微血管和检测血管边界的挑战。 这是由于以下事实：常见的卷积神经网络（CNN）无法同时保留丰富的空间信息和较大的接收场。 此外，用于血管分割的CNN模型通常采用相等像素水平的交叉熵损失进行训练，这往往会错过精细的血管结构。 在本文中，我们提出了一种用于血管分割的新型上下文空间U-Net（CSU-Net）。 与其他基于U-Net的模型相比，我们设计了两个通道的编码器：具有多尺度卷积的上下文通道可捕获更多的接收场，而具有大内核的空间通道可保留空间信息。 另外，为了合并和增强从两条路径提取的特征，我们引入了特征融合模块（FFM）和注意跳过模块（ASM）。 此外，我们提出了一种结构损失，这种结构损失增加了交叉熵损失的空间权重，并指导网络将注意力更多地集中在细血管和边界上。 我们在三个公共数据集上评估了该模型：DRIVE，CHASE-DB1和STARE。 结果表明，与当前的最新方法相比，CSU-Net具有更高的分割精度。

文章链接：https://ieeexplore.ieee.org/document/9146113