Retinal Vessel Segmentation Method Based on Position-Aware Circular Convolution with Multi-Scale Input

doi:10.3778/j.issn.1002-8331.2305-0227

Abstract

Abstract: A retinal vessel segmentation method combining position-aware circular convolution and multi-scale resolution input is proposed for the problem of complex retinal image vessel texture, extremely many tiny vessels, and low imaging contrast. The convolution module（ParC-ECA Block） with ordinary convolution, position-sensitive circular convolution, and ECA（efficient channel attention） attention is used to fully extract the global and local feature information of the input fundus image. In the cascaded downsampling path, the multi-scale input module（multi-scale input block） is proposed to enhance the feature information at each level, retrieve the lost detail information, and avoid the network performance degradation caused by the detail loss. In the jump connection using the residual dual attention module（residual spatial channel attention block, RSCA） to further improve the segmentation performance by performing background interference noise filtering and vascular feature enhancement on the basis of maintaining the original feature transfer at each level of the network. The proposed method is experimented on the DRIVE dataset and CHASE_DB1 dataset, with AUCs of 98.53% and 98.81%, ACCs of 95.81% and 96.84%, and F1-scores of 83.55% and 83.39%, respectively. The experimental results show that the proposed method outperforms the existing mainstream segmentation methods, especially in the segmentation of tiny vessels.

Key words: retinal vessel segmentation, multi-scale input, position-aware circular convolution, attention mechanism

摘要： 针对视网膜图像血管纹理复杂，微小血管极多，成像对比度低的问题，提出一种结合位置感知循环卷积（position aware circular convolution，ParC）、多尺度分辨率输入的视网膜血管分割方法。使用带有普通卷积、位置感知循环卷积、ECA（efficient channel attention）注意力的卷积模块（ParC-ECA block）来充分提取输入眼底图像的全局、局部特征信息；在级联的下采样路径中，提出多尺度输入模块（multi-scale input block）来对每一层级的特征信息进行加强，找回丢失的细节信息，避免因细节丢失而引起的网络性能下降；在跳跃连接中使用残差双注意力模块（residual spatial channel attention block，RSCA），在保持网络每一层级原始特征传递的基础上，对其进行背景干扰噪声过滤和血管特征强化，进一步提升分割性能。提出的方法在DRIVE数据集和CHASE_DB1数据集上进行了实验，其AUC分别为98.53%和98.81%，ACC分别为95.81%和96.84%，F1-score分别为83.55%和83.39%。实验结果表明所提方法优于现有主流分割方法，特别是在微小血管的分割表现方面较为突出。

关键词: 视网膜血管分割, 多尺度输入, 位置感知循环卷积, 注意力机制

JIANG Zhongchuan, WU Yun. Retinal Vessel Segmentation Method Based on Position-Aware Circular Convolution with Multi-Scale Input[J]. Computer Engineering and Applications, 2023, 59(21): 242-250.

江中川, 吴云. 位置感知循环卷积与多尺度输入的视网膜血管分割方法[J]. 计算机工程与应用, 2023, 59(21): 242-250.

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