Retinal Vessel Segmentation Based on a Lightweight U-Net and Reverse Attention

Fernando Daniel Hernandez-Gutierrez, Eli Gabriel Avina-Bravo, Mario Alberto Ibarra-Manzano, Jose Ruiz-Pinales (), Emmanuel Ovalle-Magallanes and Juan Gabriel Avina-Cervantes ()
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Fernando Daniel Hernandez-Gutierrez: Telematics and Digital Signal Processing Research Groups (CAs), Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Salamanca 36885, Mexico
Eli Gabriel Avina-Bravo: Tecnológico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing, Calle del Puente 222, Tlalpan 14380, Mexico
Mario Alberto Ibarra-Manzano: Telematics and Digital Signal Processing Research Groups (CAs), Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Salamanca 36885, Mexico
Jose Ruiz-Pinales: Telematics and Digital Signal Processing Research Groups (CAs), Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Salamanca 36885, Mexico
Emmanuel Ovalle-Magallanes: Dirección de Investigación y Doctorado, Facultad de Ingenierías y Tecnologías, Universidad La Salle Bajío, Av. Universidad 602. Col. Lomas del Campestre, León 37150, Mexico
Juan Gabriel Avina-Cervantes: Telematics and Digital Signal Processing Research Groups (CAs), Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Salamanca 36885, Mexico

Mathematics, 2025, vol. 13, issue 13, 1-27

Abstract: U-shaped architectures have achieved exceptional performance in medical image segmentation. Their aim is to extract features by two symmetrical paths: an encoder and a decoder. We propose a lightweight U-Net incorporating reverse attention and a preprocessing framework for accurate retinal vessel segmentation. This concept could be of benefit to portable or embedded recognition systems with limited resources for real-time operation. Compared to the baseline model (7.7 M parameters), the proposed U-Net model has only 1.9 M parameters and was tested on the DRIVE (Digital Retinal Images for Vesselness Extraction), CHASE (Child Heart and Health Study in England), and HRF (High-Resolution Fundus) datasets for vesselness analysis. The proposed model achieved Dice coefficients and IoU scores of 0.7871 and 0.6318 on the DRIVE dataset, 0.8036 and 0.6910 on the CHASE-DB1 Retinal Vessel Reference dataset, as well as 0.6902 and 0.5270 on the HRF dataset, respectively. Notably, the integration of the reverse attention mechanism contributed to a more accurate delineation of thin and peripheral vessels, which are often undetected by conventional models. The model comprised 1.94 million parameters and 12.21 GFLOPs. Furthermore, during inference, the model achieved a frame rate average of 208 FPS and a latency of 4.81 ms. These findings support the applicability of the proposed model in real-world clinical and mobile healthcare environments where efficiency and Accuracy are essential.

Keywords: vesselness; retinopaties; segmentation; reverse attention (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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