[1]刘延龙,吴 聪. 基于多层特征融合的视网膜血管分割模型[J].湖北工业大学学报,2022,(4):24-28.
　LIU Yanlong,WU Cong. A Multilevel Feature Fusion Model for Retinal Vessels Segmentation[J].,2022,(4):24-28.
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 基于多层特征融合的视网膜血管分割模型()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2022年第4期
页码:
24-28
栏目:
湖北工业大学学报
出版日期:
2022-08-28

文章信息/Info

Title:
 A Multilevel Feature Fusion Model for Retinal Vessels Segmentation
文章编号:
1003-4684(2022)04-0024-05
作者:
 刘延龙;  吴 聪
 湖北工业大学计算机学院， 湖北 武汉 430068
Author(s):
 LIU Yanlong; WU Cong
 School of Computer Science，Hubei Univ. of Tech.，Wuhan 430068,China
关键词:
 ; 医学图像处理;  视网膜血管分割;  连续记忆力机制;  多层特征融合
Keywords:
 ; medical image processing;  retinal vessel segmentation;  continuous memory mechanism;  multilevel feature fusion
分类号:
TP391
文献标志码:
A
摘要:
 针对现有的视网膜血管分割算法在下采样的学习层中不能有效捕捉更多特征，且低层特征没有得到充分利用从而提高视网膜血管分割的准确性，提出了一种新颖的多层特征融合网络。首先设计了一个具有连续记忆力机制的模块来增加网络的深度，以捕获更多的特征。在此基础上提出了一个多层特征融合模块将低层特征和高层特征进行融合。在DRIVE和CHASE_DB1数据集上进行训练和测试，结果表明：与其它流行的深度学习方法相比，该方法在两个数据集上获得了更好的结果，视网膜血管分割结果更加准确。
Abstract:
 Existing retinal vessel segmentation methods cannot capture more features effectively in the learning layers, and the low-level features are not fully utilized to improve the accuracy of retinal vessel segmentation. We propose a novel multilevel feature fusion network. First, we designed block with a continuous memory mechanism to increase the depth of the network to capture more features. On this basis, we proposed a multilevel feature fusion block to fuse the low-level features and the high-level features. In DRIVE and CHASE_ DB1, the results show that our method achieves better performance than other popular deep learning methods.

参考文献/References:

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备注/Memo

备注/Memo:
［收稿日期］ 2021-10-04
［第一作者］ 刘延龙（1995-），男，陕西延安人，湖北工业大学硕士研究生，研究方向为医学图像处理
更新日期/Last Update: 2022-08-29