Abstract
Detecting vessels in retinal images is crucial for various medical applications, including diagnosing and monitoring eye diseases such as diabetic retinopathy, glaucoma, and macular degeneration. This paper presents a study on applying the U-Net architecture with a VGG-16 backbone for retinal vessel segmentation trained on patched images. As a source of training images, three well-labeled datasets, DRIVE, STARE, and CHASE DB1, were used for the training of the segmentation algorithm. We implemented the task-specific data class to further divide training images into patches, and the data augmentation techniques to increase the size of training set and to promote the model’s generalization ability. Additionally, a blending technique was employed to achieve smooth predictions by blending image patches. The experimental results highlight the effectiveness of the proposed approach in accurately detecting blood vessels in retinal images, providing promising prospects for improving ophthalmic diagnosis and treatment.
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Acknowledgements
This work was supported by the European Regional Development Fund under the project AI &Reasoning (reg. no. CZ.02.1.01/0.0/0.0/15_003/0000466), and by the project of the Student Grant System, VSB – Technical University of Ostrava, Czech Republic, under the grant No. SP2023/12” Parallel processing of Big Data IX and by the project Constrained multi-objective Optimization Based on Problem Landscape Analysis funded by the Czech Science Foundation (grant no. GF22-34873K).
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Hasal, M., Pecha, M., Nowaková, J., Hernández-Sosa, D., Snášel, V., Timkovič, J. (2023). Retinal Vessel Segmentation by U-Net with VGG-16 Backbone on Patched Images with Smooth Blending. In: Barolli, L. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-031-40971-4_44
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