Deep learning for anterior segment OCT angiography automated denoising and vascular quantitative measurement

Luo, Man; Xu, Z.; Ye, Zehua; Liang, Zhendong; Xiao, Hui; Li, Yiqing; Liu, Zhidong; Zhu, Yingting; He, Yonghong; Zhuo, Yehong

doi:10.1016/j.bspc.2023.104660

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…These indicators can both be used to evaluate vascular perfusion. Many research studies calculated the VD of OCT-A images using ImageJ [ 14 , 18 , 41 , 45 , 49 , 56 , 58 – 60 , 62 , 63 , 65 , 66 , 77 , 80 – 83 ]. VD and vascular length (obtained from binarized and skeletonized images) are the most prevalent biomarkers in OCT-A image analysis [ 79 ].…”

Section: Resultsmentioning

confidence: 99%

Application of ImageJ in Optical Coherence Tomography Angiography (OCT‐A): A Literature Review

Rahimi,

Khameneh,

Riazi-Esfahani

et al. 2023

Journal of Ophthalmology

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Background. This study aimed to review the literature on the application of ImageJ in optical coherence tomography angiography (OCT-A) images. Methods. A general search was performed in PubMed, Google Scholar, and Scopus databases. The authors evaluated each of the selected articles in order to assess the implementation of ImageJ in OCT-A images. Results. ImageJ can aid in reducing artifacts, enhancing image quality to increase the accuracy of the process and analysis, processing and analyzing images, generating comparable parameters such as the parameters that assess perfusion of the layers (vessel density (VD), skeletonized density (SD), and vessel length density (VLD)) and the parameters that evaluate the structure of the layers (fractal dimension (FD), vessel density index (VDI), and lacunarity (LAC)), and the foveal avascular zone (FAZ) that are used widely in the retinal and choroidal studies), and establishing diagnostic criteria. It can help to save time when the dataset is huge with numerous plugins and options for image processing and analysis with reliable results. Diverse studies implemented distinct binarization and thresholding techniques, resulting in disparate outcomes and incomparable parameters. Uniformity in methodology is required to acquire comparable data from studies employing diverse processing and analysis techniques that yield varied outcomes. Conclusion. Researchers and professionals might benefit from using ImageJ because of how quickly and correctly it processes and analyzes images. It is highly adaptable and potent software, allowing users to evaluate images in a variety of ways. There exists a diverse range of methodologies for analyzing OCTA images through the utilization of ImageJ. However, it is imperative to establish a standardized strategy to ensure the reliability and consistency of the method for research purposes.

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