Segmentation of Retinal Blood Vessels Using Focal Attention Convolution Blocks in a UNET
Abstract
:1. Introduction
2. Materials and Methods
2.1. Preprocessing
2.1.1. Data Cleaning
2.1.2. Data Augmentation
2.2. The Model
2.2.1. Model Overview
2.2.2. UNET with FACB
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | Author | Accuracy | AUC | Precision | Recall | Specificity | F1 |
---|---|---|---|---|---|---|---|
DRIVE | Park, K.-B. et al. [29] | 97.06 | 98.68 | 83.02 | 83.46 | 98.36 | 83.24 |
Galdran, A. et al. [25] | - | 98.1 | - | - | - | - | |
Chen, D. et al. [40] | 96.22 | 98.78 | - | 85.76 | 99.32 | 81.60 | |
UNET with FACB | 97.9 | 93.6 | 91.7 | 88.1 | 99.0 | 89.9 | |
CHASEDB1 | Park, K.-B. et al. [29] | 97.36 | 98.59 | - | - | - | 81.1 |
Galdran, A. et al. [25] | - | 98.47 | - | - | - | - | |
Chen, D. et al. [40] | 98.12 | 99.25 | - | 84.93 | 99.66 | 82.73 | |
UNET with FACB | 99.1 | 97.0 | 94.8 | 94.4 | 99.5 | 94.6 | |
HRF | Park, K.-B. et al. [29] | 97.61 | 98.52 | 79.72 | - | - | 79.72 |
Galdran, A. et al. [25] | - | 98.25 | - | - | - | - | |
Tang, P. et al. [26] | 96.31 | 98.43 | - | 76.53 | 98.66 | 77 | |
UNET with FACB | 97.7 | 91.3 | 87.2 | 83.8 | 98.9 | 85.4 | |
STARE | Park, K.-B. et al. [29] | 98.76 | 99.73 | 84.17 | 83.24 | 99.38 | 83.7 |
Galdran, A. et al. [25] | - | 98.28 | - | - | - | - | |
Chen, D. et al. [40] | 97.96 | 99.53 | - | 87.93 | 99.37 | 88.36 | |
UNET with FACB | 97.9 | 93.6 | 91.7 | 88.1 | 99 | 89.9 | |
LES-AV | Galdran, A. et al. [25] | - | 97.34 | - | - | - | - |
UNET with FACB | 99.3 | 97.1 | 94.7 | 94.6 | 99.6 | 94.6 | |
IOSTAR | Guo, C. et al. [41] | 97.13 | 98.73 | - | 80.82 | 98.54 | - |
Li, X. et al. [42] | 95.44 | 96.23 | - | 73.22 | 98.02 | - | |
Wu, H. et al. [43] | 97.06 | 98.65 | - | 82.55 | 98.30 | - | |
UNET with FACB | 99.3 | 97.1 | 94.7 | 94.6 | 99.6 | 94.6 | |
ARIA (mean) | Tajbakhsh, N. et al. [44] | - | - | - | - | - | 72 |
UNET with FACB | 97.3 | 89.9 | 86.4 | 81.4 | 96.1 | 83.2 |
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Ortiz-Feregrino, R.; Tovar-Arriaga, S.; Pedraza-Ortega, J.C.; Rodriguez-Resendiz, J. Segmentation of Retinal Blood Vessels Using Focal Attention Convolution Blocks in a UNET. Technologies 2023, 11, 97. https://doi.org/10.3390/technologies11040097
Ortiz-Feregrino R, Tovar-Arriaga S, Pedraza-Ortega JC, Rodriguez-Resendiz J. Segmentation of Retinal Blood Vessels Using Focal Attention Convolution Blocks in a UNET. Technologies. 2023; 11(4):97. https://doi.org/10.3390/technologies11040097
Chicago/Turabian StyleOrtiz-Feregrino, Rafael, Saul Tovar-Arriaga, Jesus Carlos Pedraza-Ortega, and Juvenal Rodriguez-Resendiz. 2023. "Segmentation of Retinal Blood Vessels Using Focal Attention Convolution Blocks in a UNET" Technologies 11, no. 4: 97. https://doi.org/10.3390/technologies11040097