ABSTRACT
In recent years, ophthalmology, as one of the medical fields highly dependent on auxiliary imaging, has been at the forefront of the application of deep learning algorithm. The morphological changes of the choroid are closely related to the occurrence, development, treatment and prognosis of fundus diseases. The rapid development of optical coherence tomography has greatly promoted the accurate analysis of choroidal morphology and structure. Choroidal segmentation and related analysis are crucial for determining the pathogenesis and treatment strategies of eye diseases. However, currently, choroidal mainly relies on tedious, time-consuming, and low-reproducibility manual segmentation. To overcome these difficulties, deep learning methods for choroidal segmentation have been developed in recent years, greatly improving the accuracy and efficiency of choroidal segmentation. The purpose of this paper is to review the features of choroidal thickness in different eye diseases, explore the latest applications and advantages of deep learning models in measuring choroidal thickness, and focus on the challenges faced by deep learning models.
ABSTRACT
Purpose: To compare the accuracy of manual and automated binarization technique for the analysis of choroidal vasculature. Methods: This retrospective study was performed on a total of 98 eyes of 60 healthy subjects. Fovea-centered swept source optical coherence tomography (SS-OCT) scans were obtained and choroidal area was binarized using manual and automated image binarization technique separately. Choroidal vessel visualization in the binarized scans were subjectively graded (grades 0–100) by comparing them with the original OCT scan images by two masked graders. The subjective variability and repeatability was compared between two binarization method groups. Intergrader and intragrader variability was estimated using paired t-test. The degree of agreement between the grades for each observer and between the observers was evaluated using Bland–Altman plot. Results: The mean accuracy grades of the automatically binarized images were significantly (P < 0.001) higher (93.38% ± 1.70%) than that of manually binarized images (78.06% ± 2.92%). There was a statistically significant variability and poor agreement between the mean interobserver grades in the manual binarization arm. Conclusion: Automated image binarization technique is faster and appears to be more accurate in comparison to the manual method.