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.

Fig 1. Skeletonization and quantitative analysis of OCTA images.

We present an ImageJ macro that processes OCTA images of mCNV (A), (B). Our work enables users to perform quantitative analysis on skeletonized images (C), (D) of active mCNV lesions using nine computed biomarkers.

Fig 2. The OCTA image processing pipeline and its various stages.

Our algorithm takes manually cropped OCTA images as input. The pipeline follows a branched structure. In Branch 1, we denoise the image (1A), apply the Frangi filter (1B), and use Median Thresholding (1C) to measure area-related biomarkers. In Branch 2, we apply the Mexican Hat filter (2A) and Binary Skeletonization (2B) to measure vascular biomarkers like junctions, diameter, fractal dimension, and tortuosity.

Fig 3. Intermediate stages of the OCTA image as it travels down the pipeline.

(a) Input OCTA image showing the active mCNV lesion. (b) Manually delineated OCTA image. mCNV area is measured by counting the pixels within the contour. (c) The Gaussian kernel and Contrast adjustment were used for smoothing and denoising. (d) The Frangi vesselness filter and Mexican Hat filter were used to calculate Vessel Area and Vessel Density. (e) Binary skeletonization of the OCTA image using ImageJ. (f) The tagged skeleton was used to calculate the Junctions, Vessel Diameter, Tortuosity, and Fractal Dimension.