Dual Attention Multiscale Network for Vessel Segmentation in Fundus Photography

Pengshuai Yin, Yupeng Fang and Qilin Wan ()
Additional contact information
Pengshuai Yin: School of Future Technology, South China University of Technology, Guangzhou 510641, China
Yupeng Fang: School of Software Engineering, South China University of Technology, Guangzhou 510006, China
Qilin Wan: Guangdong-Hong Kong-Macao Greater Bay Area Weather Research Center for Monitoring Warning and Forecasting, Shenzhen 518000, China

Mathematics, 2022, vol. 10, issue 19, 1-15

Abstract: Automatic vessel structure segmentation is essential for an automatic disease diagnosis system. The task is challenging due to vessels’ different shapes and sizes across populations. This paper proposes a multiscale network with dual attention to segment various retinal blood vessels. The network injects a spatial attention module and channel attention module on a feature map, whose size is one-eighth of the input size. The network also uses multiscale input to receive multi-level information, and the network uses the multiscale output to gain more supervision. The proposed method is tested on two publicly available datasets: DRIVE and CHASEDB1. The accuracy, AUC, sensitivity, and specificity on the DRIVE dataset are 0.9615, 0.9866, 0.7709, and 0.9847, respectively. On the CHASEDB1 dataset, the metrics are 0.9800, 0.9892, 0.8215, and 0.9877, respectively. The ablative study further shows effectiveness for each part of the network. Multiscale and dual attention mechanism both improve performance. The proposed architecture is simple and effective. The inference time is 12 ms on a GPU and has potential for real-world applications. The code will be made publicly available.

Keywords: vessel segmentation; Mmedical image analysis; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/19/3687/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/19/3687/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:19:p:3687-:d:936595

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().