NICA课题组1篇论文被医学图像分析领域国际公认的最具影响力的学术会议--第22届医学图像计算和计算机辅助干预国际会议（The 22th Medical Image Computing Computer Assisted Intervention，MICCAI 2019）录用。今年的MICCAI会议在中国深圳召开，大会共计收稿1809篇，最终录取538篇，录取率约为30%。

此次被接受论文的第一作者为王搏同学， 论文标题为《Dual Encoding U-Net for Retinal Vessel Segmentation》。

针对眼底血管分割问题, 作者提出了双通道编码的U-Net网络，为分割问题提供了有效的解决方案。以下为论文摘要：

Retinal Vessel Segmentation is an essential step for the early diagnosis of eye-related diseases, such as diabetes and hypertension. Segmentation of blood vessels requires both sizeable receptive field and rich spatial information. In this paper, we propose a novel Dual Encoding U-Net (DEU-Net), which have two encoders: a spatial path with large kernel to preserve the spatial information and a context path with multiscale convolution block to capture more semantic information. On the top of the two paths, we introduce a feature fusion module to combine the different level of feature representation. Besides, we apply channel attention to select useful feature map in a skip connection. Furthermore, low-level and high-level prediction are combined in multiscale prediction module for a better accuracy. We evaluated this model on the digital retinal images for vessel extraction (DRIVE) dataset and the child heart and health study (CHASEDB1) dataset. Results show that the proposed DEU-Net model achieved the state-of-the-art retinal vessel segmentation accuracy on both datasets.

论文链接：https://link.springer.com/content/pdf/10.1007%2F978-3-030-32239-7.pdf