| Presentation preference | Poster presentation |
| Title | Developing automated vascular leakage segmentation in retinal vasculitis patients using different deep learning architectures |
| Purpose | Retinal vascular leakage is a crucial finding of retinal vasculitis (RV) manifest in fluorescein angiography (FA). Although the degree of retinal vascular leakage is associated with the severity of RV, there is no standard scheme to segment retinal vascular leakage specifically for RV patients. Here, we developed automated segmentation models for retinal vascular leakage in RV patients based on several deep learning architectures. |
| Methods | A total of 463 FA images from 83 patients diagnosed with RV have been used to develop the deep learning models. The vascular leakage was identified and manually segmented for each FA image. The images were split at a ratio of 60:20:20 for training, validating and testing datasets. The deep learning models were trained on DeeplabV3+, UNet++ and UNet model architectures using training and validating datasets. The models were evaluated on the testing dataset, and the model with the highest pixel-similarity dice score was selected as the best performing. |
| Results | Dice scores on the test dataset ranged from 0.5302 to 0.6279. The UNet++ model architecture was the best model for retinal vascular leakage with a dice score of 0.6279 (95% confidence interval 0.5584-0.6974). |
| Conclusion | We developed a deep learning detection and segmentation model for retinal vascular leakage in RV patients. Although a higher dice score would be required for clinical applications, deep learning is a promising tool for automated quantification of leakage in RV. |
| Conflict of interest | No |
Authors 1
| Last name | DHIRACHAIKULPANICH |
| Initials of first name(s) | D |
| Department | Department of Eye & Vision Sciences, University of Liverpool |
| City | Liverpool |
| Country | United Kingdom |
Authors 2
| Last name | Xie |
| Initials of first name(s) | J |
| Department | Department of Eye & Vision Sciences, University of Liverpool |
| City | Liverpool |
| Country | United Kingdom |
Authors 3
| Last name | Chen |
| Initials of first name(s) | X |
| Department | Xiamen Eye Center of Xiamen University |
| City | Xiamen |
| Country | China |
Authors 4
| Last name | Li |
| Initials of first name(s) | X |
| Department | Xiamen Eye Center of Xiamen University |
| City | Xiamen |
| Country | China |
Authors 5
| Last name | Madhusudhan |
| Initials of first name(s) | S |
| Department | St Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust |
| City | Liverpool |
| Country | United Kingdom |
Authors 6
| Last name | Zheng |
| Initials of first name(s) | Y |
| Department | Department of Eye & Vision Sciences, University of Liverpool |
| City | Liverpool |
| Country | United Kingdom |
Authors 7
| Last name | Beare |
| Initials of first name(s) | NAV |
| Department | Department of Eye & Vision Sciences, University of Liverpool |
| City | Liverpool |
| Country | United Kingdom |
