Spatial attention U-Net model with Harris hawks optimization for retinal blood vessel and optic disc segmentation in fundus images

• Published in: International ophthalmology Vol. 44; no. 1; p. 359
• Main Authors: Kumar, Puranam Revanth, Shilpa, B., Jha, Rajesh Kumar, Chellibouina, Veni Sree
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 29-08-2024; Springer Nature B.V
• Subjects: Accuracy; Adaptive sampling; Algorithms; Blood vessels; Data augmentation; Datasets; Diagnostic systems; Eye; Eye (anatomy); Feature maps; Fundus Oculi; Humans; Image processing; Image segmentation; Medicine; Medicine & Public Health; Neural Networks, Computer; Ophthalmology; Optic Disk - blood supply; Optic Disk - diagnostic imaging; Optimization; Original Paper; Retina; Retinal Vessels - diagnostic imaging; Segmentation; Vascular system; Fundus image; SAU-Net-HHO; Human eye; Retinal blood vessel & disc segmentation
• ISSN: 1573-2630; 0165-5701; 1573-2630
• DOI: 10.1007/s10792-024-03279-3

Background
The state of the human eye’s blood vessels is a crucial aspect in the diagnosis of ophthalmological illnesses. For many computer-aided diagnostic systems, precise retinal vessel segmentation is an essential job. However, it remains a difficult task due to the intricate vascular system of the eye. Although many different vascular segmentation techniques have already been presented, additional study is still required to address the problem of inadequate segmentation of thin and tiny vessels.
Methods
In this work, we introduce the Spatial Attention U-Net (SAU-Net) model with harris hawks’ optimization (HHO), a lightweight network that can be applied as a data augmentation technique to improve the efficiency of the existing annotated samples without the need of thousands of training instances for Retinal Blood Vessel and Optic Disc Segmentation. The SAU-Net-HHO implementation uses a spatially inferred attention map multiplied by the input feature map for adaptive feature enhancement. U-Net convolutional blocks have been replaced with structured dropout blocks in the proposed network to prevent overfitting. Data from both vascular extraction (DRIVE) and structured analysis of the retina (STARE) are used to evaluate SAU-Net-HHO performance.
Results
The results show that the proposed SAU-Net-HHO performs well on both datasets. Analysing the obtained results, an average of 98.5% accuracy and Specificity 96.7% was achieved for DRIVE dataset and 97.8% accuracy and specificity 94.5% for STARE dataset. The proposed method yields numerical results with average values that are on par with those of state-of-the-art methods.
Conclusion
Visual inspection has revealed that the suggested method can segment thin and tiny vessels with greater accuracy than previous methods. It also demonstrates its potential for real-life clinical application.