文献类型：会议论文

英文题名：Retinal Vascular Segmentation Network with Connectivity Guidance

作者：Wu, Ronghua[1]; Tao, Chen[1]; Chen, Hongyu[1]; Xu, Wenjing[1]; Yan, Xiao[2]; Liu, HongZhe[3]; Zhang, XiuMing[4]; Jian, Muwei[5]

第一作者：Wu, Ronghua

机构：[1] Linyi University, School of Information Science and Engineering, Linyi, China; [2] The First Affiliated Hospital of Ningbo University, Ningbo Clinical Research Center for Hematologic Malignancies, Department of Haematology, Ningybo, China; [3] Beijing Union University, Beijing Key Laboratory of Information Service Engineering, Beijing, China; [4] The First Affiliated Hospital of Zhejiang University College of Medicine, Department of Pathology, Hangzhou, China; [5] Shandong University of Finance and Economics, School of Computer Science and Technology, Jinan, China

第一机构：Linyi University, School of Information Science and Engineering, Linyi, China

通讯机构：[5]Shandong University of Finance and Economics, School of Computer Science and Technology, Jinan, China

会议论文集：Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023

会议日期：August 28, 2023 - August 31, 2023

会议地点：Portsmouth, United kingdom

语种：英文

外文关键词：Convolutional neural network; medical image processing; retinal vessel segmentation

摘要：The morphology of the retinal vasculature is closely related to a variety of ophthalmic diseases. Despite significant progresses have been achieved in retinal vessel segmentation with the development of deep learning, several challenging problems still persist. Specifically, some components present in the retina (such as optic discs or lesions) may interfere with or cover the vessels, and current segmentation models rarely focus on the connectivity of the vessels. In this paper, we propose an efficient network to conquer this issue. Firstly, we propose a semantic enhancement module, to insert into the bottom layer of the UNet encoder section for guiding the network to enhance features representation. Secondly, we introduce a skeleton fitting module that maximizes the preservation of the vascular topology using skeleton prior and contrast loss. Finally, we perform a morphological complementation operation on the final output of the decoder by means of a voting mechanism. The designed segmentation network has been evaluated on publicly available datasets and experimental results demonstrate the effectiveness of the method. ? 2023 IEEE.