## 4 Discussion  
The feature of exudative AMD is its development of CNV with associated exudation in the form of intra-/subretinal fluid and subretinal hemorrhage. Subclinical CNV is an abnormal choroidal vessel, passing through breaks in Bruch's membrane in the eyes, that experiences no overlying exudation or hemorrhage. The rate of conversion of subclinical CNV to exudative AMD has been studied. In two recent prospective cohort studies [22; 23], the risk of exudation of the eyes through subclinical CNV was shown to be approximately 15 times greater when compared with eyes in the absence of subclinical CNV. Given the noninvasive and time-efficient nature of OCTA imaging in relation to conventional angiography, it can be used to provide images of the choroidal vasculature, and may detect subclinical CNV which is asymptomatic and occurs prior to the development of exudation.  
In order to evaluate the diagnostic accuracy of OCTA, several studies have investigated the sensitivity and specificity of CNV detection with OCTA, as compared to FA. The sensitivity rate from OCTA ranged from 81% to 87%, with the corresponding specificities being 91-98% [24; 25]. Therefore, the use of OCTA imaging could be considered as a reliable tool for CNV diagnosis and follow-up.  
An accurate segmentation and quantification of CNV is required during both disease diagnosis and follow up. Up until now, CNV has been delineated manually and is an operator-dependent task. Generally, vessel segmentation methods include an enhancement of vascular structures and the separation of tubular components of the image into vessel and non-vessel structures [26]. After undergoing two Otsu methods, the blood vessels can be separated from the background. Unfortunately they will still contain some noise; however, this becomes very clear when compared to the noise of the original image. The biggest disadvantage of the Otsu method is that when the CNV is the same as the noise value, it can never be separated. For these noise points that interfere with the blood vessel region, we use a Majority and Size filter to solve this problem, as this is very effective in both eliminating noise and retaining the blood vessel region as best as possible.  
Although efforts are always made to protect the blood vessel region from being removed during the process of eliminating noise, there still remains a small number of broken blood vessels. The thinning of morphology and endpoint connection can effectively solve the problem of broken blood vessels.  
Figure 9: Some final detection results. CNV was found in Figure (**a**)–(**d**) but not found in Figure (**e**).  
Finally, using the eccentricity as a feature of the resolution CNV, we can find the CNV in most images. But a few CNVs cannot be found with eccentricity. This also means that our method cannot find a CNV with an indeterminate shape. At the same time, it will not locate areas that are difficult to define. In practice, this can reduce the possibility of excessive misjudgments.  
There were some limitations of our study. First, we used a relatively small sample size, so further studies on a larger scale should be performed in order to evaluate the sensitivity and specificity of our method. Second, some artifacts such as motion artifacts and projection artifacts may also limit the detection accuracy and quantification of CNV. However, we used ICGA to verify the CNV complexes, which is compatible with the OCTA findings in our method. This proved to be the strength within our study.