Application of Novel Down-sampling Method in Retinal Vessel Segmentation.
10.3969/j.issn.1671-7104.2023.01.006
- Author:
Zhijin LYU
1
;
Xuefang CHEN
1
;
Xiaofang ZHAO
2
;
Huazhu LIU
2
Author Information
1. School of Computer Science and Technology, Dongguan Institute of Technology, Dongguan, 523429.
2. School of Electronic Engineering and Intelligence, Dongguan Institute of Technology, Dongguan, 523429.
- Publication Type:Journal Article
- Keywords:
PF-pooling;
convolutional neural networks;
deep learning;
down-sampling;
retinal vessel segmentation
- MeSH:
Algorithms;
Retinal Vessels;
Image Processing, Computer-Assisted
- From:Chinese Journal of Medical Instrumentation
2023;47(1):38-42
- CountryChina
- Language:Chinese
-
Abstract:
Accurate segmentation of retinal blood vessels is of great significance for diagnosing, preventing and detecting eye diseases. In recent years, the U-Net network and its various variants have reached advanced level in the field of medical image segmentation. Most of these networks choose to use simple max pooling to down-sample the intermediate feature layer of the image, which is easy to lose part of the information, so this study proposes a simple and effective new down-sampling method Pixel Fusion-pooling (PF-pooling), which can well fuse the adjacent pixel information of the image. The down-sampling method proposed in this study is a lightweight general module that can be effectively integrated into various network architectures based on convolutional operations. The experimental results on the DRIVE and STARE datasets show that the F1-score index of the U-Net model using PF-pooling on the STARE dataset improved by 1.98%. The accuracy rate is increased by 0.2%, and the sensitivity is increased by 3.88%. And the generalization of the proposed module is verified by replacing different algorithm models. The results show that PF-pooling has achieved performance improvement in both Dense-UNet and Res-UNet models, and has good universality.
