# Quantitative Analysis of Retinal Vascular Leakage in Retinal Vascular Using Machine Learning
## 4 Discussion  
FA is the gold standard in detection of inflammatory lesions in the posterior segment, such as in retinal vascular leakage. FA is useful for the evaluation of disease activity and severity, and for monitoring during treatment [7]. However, since the evaluation of FA findings is subjective and descriptive, quantitative analysis of vascular leakage is required. In the current study, we explored the feasibility of automatic segmentation of retinal vascular leakage in UWF FA images in patients with uveitis. Our study implemented a deep learning method to extract retinal vessels using the U-Net system, and automated segmentation of retinal vascular leakage was performed. Subsequently, vascular leakage areas were quantified automatically, and the FA score was significantly associated with the automated segmented area (pixels) of retinal vascular leakage. Retinal vascular leakage areas were detected by subtraction of early and late-phase images in the current study. Registration was applied by using vascular feature points as key points; thus, vascular segmentation was especially important. Recent reports have demonstrated an automated tool to extract retinal vasculature using a deep learning algorithm [22; 23]. In the current study, we used a deep learning system, the U-Net system,  
Figure 6: Comparison FA score (**a**) and mean pixels of automatic segmented vascular leakage in FA images (**b**) before and after treatment from six patients. Asterisk indicates \(p\) < 0.05 versus FA score or the pixels of automatic segmented vascular leakage in FA images before and after treatment. Statistical analysis was performed with the paired-\(t\) test. for segmentation of retinal vessels in early-phase and late-phase UWF FA images. For learning using the U-Net network, the DRIVE and STARE databases were used as training data, rather than using UWF fundus images and UWF FA images as the training data set, suggesting that a supervised FA dataset is not required and can be replaced by transforming RGB color images with the supervised dataset. The whole region tends to be white in FA image with strong leakage, and only a part of the vascular area is described. The mean accuracy of vascular segmentation in early-phase FA images was 0.970. On the other hand, reported accuracies of color retinal images in recent years have been 0.97-1.0. U-Net training without target images achieved a good enough performance for the current study. The automated segmentation of retinal vessels using the U-net system in the present study may be feasible for the assessment and quantification of retinal vascular features in not only uveitis, but also other retinal diseases, such as diabetic retinopathy. Three well-known registration methods, Oriented FAST and Rotated BRIEF (ORB) [24], KAZE, and Accelerated-KAZE (AKAZE) [25], were discussed for the current study. ORB performs as well as SIFT for feature detection and works rapidly. AKAZE is based on nonlinear diffusion filtering like KAZE, and it works faster than KAZE. For assessment of performance level of registration methods, the Dice coefficient was compared between the automatic segmented vascular areas in early and late-phase FA images. Mean Dice coefficients of ORB, KAZE, and AKAZE in 12 FA images were 0.469 \(\pm\) 0.149 (0.105-0.658), 0.577 \(\pm\) 0.111 (0.367-0.717), and 0.567 \(\pm\) 0.111 (0.337-0.713), respectively. The current study applied KAZE, which had the highest Dice coefficient, although mean values of KAZE and AKAZE were similar. We conducted a paired t-test for Dice coefficients of KAZE and AKAZE and found no significant difference between the conditions (\(p\) = 0.15 \(>\) 0.05). Note that given the lower numbers of FA image pairs, this may be attributable to a lack of power. The reason for the low Dice coefficient was that the visible vascular area in the late-phase FA images was lower than that in the early images due to high vascular leakage. In our vision assessment, all 12 subtraction images showed good enough results for leakage segmentation.