MSR U-Net: An Improved U-Net Model for Retinal Blood Vessel Segmentation

被引：0

作者：

Kande, Giri Babu ^{[1
]}

Ravi, Logesh ^{[2
,3
]}

Kande, Nitya ^{[4
]}

Nalluri, Madhusudana Rao ^{[5
]}

Kotb, Hossam ^{[6
]}

Aboras, Kareem M. ^{[6
]}

Yousef, Amr ^{[7
,8
]}

Ghadi, Yazeed Yasin ^{[9
]}

Sasikumar, A. ^{[10
]}

机构：

[1] Vasireddy Venkatadri Institute of Technology, Nambur, Guntur,522508, India

[2] Vellore Institute of Technology, Centre for Advanced Data Science, Tamil Nadu, Chennai,600127, India

[3] Vellore Institute of Technology, School of Electronics Engineering, Tamil Nadu, Chennai,600127, India

[4] VIT-AP University, Amaravathi,522237, India

[5] Amrita Vishwa Vidyapeetham, Kuragallu, School of Computing, Guntur,522503, India

[6] Alexandria University, Faculty of Engineering, Department of Electrical Power and Machines, Alexandria,21544, Egypt

[7] Alexandria University, Faculty of Engineering, Engineering Mathematics Department, Alexandria,21544, Egypt

[8] University of Business and Technology, Al Rawdah, Electrical Engineering Department, Jeddah,23435, Saudi Arabia

[9] Al Ain University, Department of Computer Science and Software Engineering, Abu Dhabi, United Arab Emirates

[10] SRM Institute of Science and Technology, Faculty of Engineering and Technology, Department of Data Science and Business Systems, Tamil Nadu, Kattankulathur,603203, India

来源：

IEEE Access | 2024年 / 12卷

关键词：

Blood vessels - Complex networks - Convolution - Diagnosis - Feature extraction - Image enhancement - Medical imaging - Neural networks - Ophthalmology;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

For the proper diagnosis and treatment of a variety of retinal conditions, retinal blood vessel segmentation is crucial. Delineation of vessels with varying thicknesses is critical for detecting disease symptoms. However, this task is challenging due to inadequate contextual information, complex vessel morphology, and lesion confusion. Many recent works employed several variations of CNNs with U-Net as baseline model for segmenting blood vessels from the fundus images. However, the existing methods still lack in generalizing the vessels well enough, indicating scope for improvement in this challenging problem of vessel segmentation. We introduce a novel Multi-Scale Residual (MSR) U-Net model in this study replacing convolution block and skip connections with an improved Multi-Scale Residual (MSR) convolution block and Bottleneck residual paths (B-Res paths) respectively. Specifically, STARE, DRIVE, and CHASEDB1 datasets of fundus images are used to validate the proposed segmentation method. Our experimental results consistently showcase better/comparable performances when compared with current approaches, achieving higher area under receiver operator characteristic (AUC), accuracy, and F1 score. in segmenting blood vessels of varying thicknesses, even in scenarios with diverse contextual information, the presence of coexisting lesions, and complex vessel morphologies. © 2023 The Authors.

引用

页码：534 / 551