In this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL) and logistic regression (LR) were compared in terms of the classification accuracy for automated glaucoma detection. The performance of the ML models was evaluated on unseen test data, n = 55. An image segmentation pilot study was then performed on cross-sectional OCT scans. The ONH cup area was extracted, analyzed, and a new DL model was trained for glaucoma prediction. The DL model was estimated using five-fold cross-validation and compared with two pre-trained models. The DL model trained from the optimal features achieved significantly higher diagnostic performance (area under the receiver operating characteristic curve (AUC) 0.98 and accuracy of 97% on validation data and 96% on test data) compared to previous studies for automated glaucoma detection. The second DL model used in the pilot study also showed promising outcomes (AUC 0.99 and accuracy of 98.6%) to detect glaucoma compared to two pre-trained models. In combination, the result of the two studies strongly suggests the four features and the cross-sectional ONH cup area trained using deep learning have a great potential for use as an initial screening tool for glaucoma which will assist clinicians in making a precise decision.

Optical Coherence Tomography (OCT) is a popular non-invasive clinical tool for the diagnosis of ocular diseases that provides micron-scale images of ocular pathology in vivo and in real-time. The cross-sectional OCT B-scan of Temporal-Superior-Nasal-Inferior-Temporal (TSNIT) peripapillary retinal profile is widely used to diagnose and monitor glaucoma. However, raw OCT images can be marred by noise and artifacts, especially vitreoretinal interface opacity: this can lead to segmentation error, misinterpretation of retinal thickness measurements and possibly inappropriate glaucoma management. In this study, we designed and trained a U-Net model on OCT B-scans with artifacts, and their corresponding 'artifact-free B-scans'. The U-Net was able to remove the artifacts successfully with better performance in terms of PSNR and SSIM values. The SNR of the OCT scans with speckle noise associated with artifacts has also been improved. To the best of our knowledge, this is the first study where automated vitreous opacity artifact removal has been applied to the TSNIT profile. The performance of the U-net model on measures such as PSNR, SSIM, MAE, and MSE is compared with the state-of-the-art image denoising models. It is observed that the proposed U-Net model performs better as compared to the other models on both parametric and visual evaluations. In the future, this U-Net model could be used to solve automatic retinal layer segmentation errors and assist clinicians in interpreting OCT images in glaucoma diagnosis and monitoring.

We have developed a methodology for reconstruction of three-dimensional retinal vascular structure from optical coherence tomography angiography images to determine whether characteristic glaucomatous vascular optic nerve head changes can be visualized in this modality.

In order to achieve a nanometer-scale resolution in an x-ray microscopy system, a Gabor-type hologram was produced by eliminating the zero-order term of the object diffraction pattern. In this system, a Fresnel zone plate was used for strong illumination of an object, and the zero-order diffraction was physically eliminated by a center stop. An accurate phase plate of π / 2 in the Zernike method was numerically created, and the phase-contrast image was realized. The theoretical resolution was 19.8 nm. We proved that a gold nanocube with a size of 50 nm can be reconstructed with the predicted resolution.