## 2 Materials and Methods
Following identification of the fovea on high magnification module images, we used the \"Concentric Circle\" plugin to generate circles with a radius of 1 to 10 degrees, centered on the fovea on the high magnification module [18]. One degree was assumed to be equal to 291 uM [21]. We manually selected regions of interest (ROIs) of 100 \(\times\) 100 uM at 3, 6 and 9 degrees in the temporal gaze direction, avoiding blood vessels and imaging artifacts. Manual counts were performed by two independent human graders using the \"Cell Count\" plugin [18]. All ROIs were pseudonymized and processed twice by each grader at two different days with a period of at least 7 days in between. This resulted in a total of four counts per ROI. Both graders were instructed to count cellular structures with a round or polygonal shape, regardless of the size. We used examples of manually counted high magnification module images from the study of Mendonca as training images [10]. Cells that crossed ROI boundaries, and were thus not fully visible, were excluded from manual counting. We then designed a high-pass filter in the Fiji software. We first filtered the original high magnification module image with a Gaussian blur with a predefined sigma radius via the \"Gaussian Blur\" plugin [18]. We subtracted Gaussian blurred high magnification module images from their original counterparts using the \"Image Calculator\" plugin [18]:  
\[I^{\prime}_{(x_{n})}=I_{(x_{n})}-\left(I_{(x_{n})}*G\left(x,y\right)\right) \tag{1}\]  
\(I^{\prime}_{(x_{n})}\) is the high-pass filtered high magnification module image, \(I_{(x_{n})}\) is the original high magnification module image, \({}^{\star}\) represents the convolution operator and \(G\left(x,y\right)\) is the two dimensional Gaussian function. Consequently, only signals above the threshold of the Gaussian blurred image remained in the resulting image. We normalized local contrast in the final image via the \"Integral Image Filter\" plugin by applying a default X/Y block with a radius of 40 pixels and a standard deviation of 3 [18]. Our Gaussian blur utilizes a two-dimensional Gaussian function, represented by the formula:  
\[G\left(x,\ y\right)=\frac{1}{2\pi\sigma^{2}}e^{-\frac{x^{2}+y^{2}}{2\sigma^{2}}} \tag{2}\]  
\(x\) and \(y\) are the location indices of the horizontal and vertical axis, respectively, and \(\sigma\) represents the standard deviation of the Gaussian distribution. By increasing the Gaussian sigma radius, a larger image area is blurred. Therefore, adjustment of the sigma radius affects the threshold of the high-pass filter and consequently alters the detection of photoreceptor signals. To optimize the predefined Gaussian sigma radius, we applied a trial-and-error approach and compared the automated cone counts from several distinct sigma radii to the average cone counts of the human graders. We used the \"3D Maxima Finder\" plugin to automatically detect cone photoreceptors on high-pass filtered images [22]. This maxima finder enables users to specify the radius of maxima per ROI. The 3D Maxima Finder radii we selected were based upon previously published cone inner segment diameters [23]. Prior to the analysis, all images were converted to 16-bit for analytical purposes. Maxima located on the edge of ROIs were automatically excluded from the analysis. Results were evaluated based on absolute cell counts obtained by the manual and automated counting of cells in ROIs of 0.01 mm\({}^{2}\). We analyzed intersession and intrasession differences between human graders using an intraclass correlation (ICC) estimate via a Single-Measurements, Absolute-Agreement, 2-Way Random-Effects model in SPSS version 25 (IBM, Armonk, NY, USA). The same model was used to compare the results of automatically counted, filtered images to the average of manual cone counts. By conducting a Bland-Altman analysis, we studied the intergrader and intragrader differences as well as the difference between human graders and computer-assisted, high-pass filtered counts.