An ImageJ macro tool for OCTA-based quantitative analysis of Myopic Choroidal neovascularization

Abstract

Myopic Choroidal neovascularization (mCNV) is one of the most common vision-threatening com- plications of pathological myopia among many retinal diseases. Optical Coherence Tomography Angiography (OCTA) is an emerging newer non-invasive imaging technique and is recently being included in the investigation and treatment of mCNV. However, there exists no standard tool for time-efficient and dependable analysis of OCTA images of mCNV. In this study, we propose a customizable ImageJ macro that automates the OCTA image processing and lets users measure nine mCNV biomarkers. We developed a three-stage image processing pipeline to process the OCTA images using the macro. The images were first manually delineated, and then denoised using a Gaussian Filter. This was followed by the application of the Frangi filter and Local Adaptive thresholding. Finally, skeletonized images were obtained using the Mexican Hat filter. Nine vascular biomarkers including Junction Density, Vessel Diameter, and Fractal Dimension were then computed from the skeletonized images. The macro was tested on a 26 OCTA image dataset for all biomarkers. Two trends emerged in the computed biomarker values. First, the lesion-size dependent parameters (mCNV Area (mm2) Mean = 0.65, SD = 0.46) showed high variation, whereas normalized parameters (Junction Density(n/mm): Mean = 10.24, SD = 0.63) were uniform throughout the dataset. The computed values were consistent with manual measurements within existing literature. The results illustrate our ImageJ macro to be a convenient alternative for manual OCTA image processing, including provisions for batch processing and parameter customization, providing a systematic, reliable analysis of mCNV.

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

“…However, OCT-A images often contain various artifacts and noise, which can affect the accuracy of the interpretation of the vascular images [ 5 ]. This is where ImageJ comes in, playing a crucial role in removing these unwanted artifacts [ 12 – 15 ] Also, it can be used to diminish noise, increase image quality, and enhance details [ 16 – 20 ]. ImageJ has several plugins that are designed specifically to remove artifacts and noise in OCT-A images, such as the “rolling ball background subtraction” plugin [ 21 ], the “bandpass filter” plugin [ 22 ], and the “image calculator” plugin [ 23 ].…”

“…Filtering involves smoothing the image to reduce noise and enhance the signal. ImageJ has several filters, including the Gaussian and Median filters, that can be used to remove noise and enhance image quality [ 16 , 30 , 31 ]. Once the motion artifacts have been removed, the OCT-A images can be further processed and analyzed.…”

A Complete Review of Automatic Detection, Segmentation, and Quantification of Neovascularization in Optical Coherence Tomography Angiography Images

“…The Frangi filter [49] was a popular choice for detecting vessels within a specific size range. It was used by Deshpande et al [48] and Taibouni et al [28] and was effective when vessels within lesions were distinguishable. Therefore, it was unsuitable for NV lesions with a cotton wool texture or those where vessels were not separable.…”