A hybrid deep segmentation network for fundus vessels via deep-learning framework & nbsp;
文献类型：期刊论文
| 作者 | Yang, Lei1,2 ; Wang, Huaixin1,2; Zeng, Qingshan1,2; Liu, Yanhong1,2; Bian, Guibin1,3
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2021-08-11 |
| 卷号 | 448页码:168-178 |
| 关键词 | Image segmentation Fundus vessels U-net network Multitask segmentation network Fusion network |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2021.03.085 |
| 通讯作者 | Liu, Yanhong() |
| 英文摘要 | High-precision segmentation of fundus vessels is a fundamental step in the diagnosis and treatment of fundus diseases, in which both thick and thin vessels are important features for symptom detection. With the rapid development of artificial intelligence, the deep convolutional neural network (DCNN) has been widely applied into image analysis of fundus vessels. Nevertheless, due to the imbalanced ratio between thick and thin vessels, the existing segmentation methods are weak in the task of microvessel extraction from fundus images. To address this problem, this paper proposes a new hybrid deep image segmentation method for fundus vessels that consists of a multitask segmentation network and a fusion network. For the proposed method, a multitask segmentation network is developed to precisely segment both thick vessels and thin vessels from fundus images separately. In addition, an effective loss function is designed to adapt to the two different vessel segmentation tasks and ultimately solve the imbalanced ratio between these two vessels. Furthermore, an improved U-net network model is proposed to serve as the basic segmentation network to ensure the segmentation performance of the multitask segmentation network. Together with these networks, a fusion network is also proposed to fuse these two kinds of blood vessels to obtain the fusion images as the final segmentation results of fundus vessels. The proposed segmentation method is validated on many different public data sets of fundus images, such as DRIVE, STARE and CHASE_DB1. Experimental results show that the proposed method obtains a better segmentation performance on fundus images and acquires a higher recall, F_1 value, and accuracy than other advanced segmentation methods. (c) 2021 Elsevier B.V. All rights reserved. |
| WOS关键词 | BLOOD-VESSELS ; QUANTITATIVE-ANALYSIS ; NEURAL-NETWORK ; RETINAL IMAGES ; FILTER |
| 资助项目 | National Key Research & Development Project of China[2020YFB1313701] ; National Natural Science Foundation of China[62003309] ; Science & Technology Research Project in Henan Province of China[202102210098] ; Outstanding Foreign Scientist Support Project in Henan Province of China[GZS2019008] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000652811900015 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Research & Development Project of China ; National Natural Science Foundation of China ; Science & Technology Research Project in Henan Province of China ; Outstanding Foreign Scientist Support Project in Henan Province of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/45206]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 通讯作者 | Liu, Yanhong |
| 作者单位 | 1.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Peoples R China 2.Robot Percept & Control Engn Lab, Zhengzhou 450001, Henan, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Lei,Wang, Huaixin,Zeng, Qingshan,et al. A hybrid deep segmentation network for fundus vessels via deep-learning framework & nbsp;[J]. NEUROCOMPUTING,2021,448:168-178. |
| APA | Yang, Lei,Wang, Huaixin,Zeng, Qingshan,Liu, Yanhong,&Bian, Guibin.(2021).A hybrid deep segmentation network for fundus vessels via deep-learning framework & nbsp;.NEUROCOMPUTING,448,168-178. |
| MLA | Yang, Lei,et al."A hybrid deep segmentation network for fundus vessels via deep-learning framework & nbsp;".NEUROCOMPUTING 448(2021):168-178. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。