Open Access

ARTICLE

MIA-UNet: Multi-Scale Iterative Aggregation U-Network for Retinal Vessel Segmentation

Linfang Yu, Zhen Qin^*, Yi Ding, Zhiguang Qin

Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China

* Corresponding Author: Zhen Qin. Email:

(This article belongs to the Special Issue: Internet of Things in Healthcare and Health: Security and Privacy)

Computer Modeling in Engineering & Sciences 2021, 129(2), 805-828. https://doi.org/10.32604/cmes.2021.017332

Received 02 May 2021; Accepted 23 June 2021; Issue published 08 October 2021

Abstract

As an important part of the new generation of information technology, the Internet of Things (IoT) has been widely concerned and regarded as an enabling technology of the next generation of health care system. The fundus photography equipment is connected to the cloud platform through the IoT, so as to realize the real-time uploading of fundus images and the rapid issuance of diagnostic suggestions by artificial intelligence. At the same time, important security and privacy issues have emerged. The data uploaded to the cloud platform involves more personal attributes, health status and medical application data of patients. Once leaked, abused or improperly disclosed, personal information security will be violated. Therefore, it is important to address the security and privacy issues of massive medical and healthcare equipment connecting to the infrastructure of IoT healthcare and health systems. To meet this challenge, we propose MIA-UNet, a multi-scale iterative aggregation U-network, which aims to achieve accurate and efficient retinal vessel segmentation for ophthalmic auxiliary diagnosis while ensuring that the network has low computational complexity to adapt to mobile terminals. In this way, users do not need to upload the data to the cloud platform, and can analyze and process the fundus images on their own mobile terminals, thus eliminating the leakage of personal information. Specifically, the interconnection between encoder and decoder, as well as the internal connection between decoder sub-networks in classic U-Net are redefined and redesigned. Furthermore, we propose a hybrid loss function to smooth the gradient and deal with the imbalance between foreground and background. Compared with the U-Net, the segmentation performance of the proposed network is significantly improved on the premise that the number of parameters is only increased by 2%. When applied to three publicly available datasets: DRIVE, STARE and CHASE_DB1, the proposed network achieves the accuracy/F1-score of 96.33%/84.34%, 97.12%/83.17% and 97.06%/84.10%, respectively. The experimental results show that the MIA-UNet is superior to the state-of-the-art methods.

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Keywords

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