The imported OCT volume was rectified by applying ImageJ's “Flip Vertically” command, then the “Flip Horizontally” command, to the entire stack. The imported OCT volume appeared horizontally and vertically inverted. Image data were exported from the Ple圎lite device as IMG and imported into ImageJ using the Import>Raw command with parameters: 8-bit data, width: 1024 pixels, height: 1535 pixels, offset to first image: 0 bytes, number of images: 1024, gap between images: 0 bytes, white is zero: Yes, little-endian byte order: Yes. ![]() Scans with eye movement artifacts or excessively low signal-to-noise ratio without recognizable liquid vitreous spaces on individual B-scans precluding 3D reconstruction were excluded from the analysis. Depending on subject cooperativity, one or both eyes were imaged. ![]() Vitreous imaging was performed on at least one eye of each participant, based on individual preference and ease of acquisition. During scan acquisition, visualization of vitreous structures was prioritized by positioning the retina and choroid OCT signal posteriorly within the scan window. Cube (12 × 12 mm) scans comprising 1024 B-scans (each comprising 1024 A-scans) with a scan depth of 6 mm centered at the fovea were obtained for each pharmacologically dilated study eye (phenylephrine HCl 2.5% and tropicamide 1%). The PLEX Elite 9000 uses a swept-source laser with a center wavelength of 1040 to 1060 nm and a scan speed of 100,000 A-scans per second. ![]() All these factors add potential confounding factors and limit the ability to correlate anatomy with pathology.Īll eyes were imaged on the PLEX Elite 9000 (Carl Zeiss Meditec, Inc) OCT device. Furthermore, these delicate structures are prone to damage during handling and preparation, as well as postmortem decomposition. Although these methods produced valuable information, they did not allow for examining the vitreous in vivo. This was probably due in large part to their high optical transmittance. In addition to the prepapillary gap 4, 5 in the area of Martegiani, these methods revealed various liquid vitreous spaces, such as the premacular bursa 1, 2 or posterior precortical vitreous pocket, 3 and prevascular vitreous fissures 4, 5 and cisterns, 1, 2 which had not been previously detected. Worst, 1, 2 Kishi and Shimizu, 3 Eisner, 4, 5 and Sebag 6 have visualized the vitreous body in three dimensions (3D) by carefully preparing cadaver eyes and often staining or injecting them with dye. The anatomy of the vitreous is notoriously difficult to visualize in vivo and postmortem. This machine learning model now allows for comprehensive examination of the vitreous structure beyond the vitreoretinal interface in 3D with potential applications for common disease states such as the vitreomacular traction and Macular Hole spectrum of diseases or proliferative diabetic retinopathy. The resultant high-resolution 3D movies illustrated vitreous anatomy in a manner like triamcinolone-assisted vitrectomy or postmortem dye injection. Clinically relevant vitreous features including the premacular bursa, area of Martegiani, and prevascular vitreous fissures and cisterns, as well as varying degrees of vitreous degeneration were visualized in 3D.Ī machine-learning model for 3D vitreous reconstruction of SS-OCT cube scans was developed. Thirty-four cube scans from 25 subjects were of sufficient quality for volume rendering. Volume rendering was performed with TomViz.įorty-seven eyes of 34 healthy subjects were imaged with SS-OCT. Thresholding was performed to remove pixels that were classified with low confidence. Results were generated as a probability map. Two classes were defined: “Septa” and “Other.” Pixels were selected and added to each class to train the classifier. Image-averaging and Trainable Weka Segmentation using Sobel and variance edge detection and directional membrane projections filters were used to enhance and interpret the signals from vitreous gel, liquid spaces within the vitreous, and interfaces between the former. Scans of sufficient quality were transferred into the Fiji is just ImageJ image processing toolkit, and proportions of the resulting stacks were adjusted to form cubic voxels. ![]() Healthy subjects were imaged with SS-OCT. To develop a machine-learning image processing model for three-dimensional (3D) reconstruction of vitreous anatomy visualized with swept-source optical coherence tomography (SS-OCT).
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