A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device (Spectralis) for both eyes of 20 subjects. For each eye, single-frame (without signal averaging), and multi-frame (75x signal averaging) volume scans were obtained. A custom deep learning network was then designed and trained with 2,328 "clean B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance of the de-noising algorithm was assessed qualitatively, and quantitatively on 1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio (CNR), and mean structural similarity index metrics (MSSIM). Results: The proposed algorithm successfully denoised unseen single-frame OCT B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR increased from $4.02 \pm 0.68$ dB (single-frame) to $8.14 \pm 1.03$ dB (denoised). For all the ONH tissues, the mean CNR increased from $3.50 \pm 0.56$ (single-frame) to $7.63 \pm 1.81$ (denoised). The MSSIM increased from $0.13 \pm 0.02$ (single frame) to $0.65 \pm 0.03$ (denoised) when compared with the corresponding multi-frame B-scans. Conclusions: Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort

Multidisciplinary Ophthalmic Imaging Automated Analysis of Angle Closure From Anterior Chamber Angle Images

PURPOSE. To evaluate a novel software capable of automatically grading angle closure on EyeCam angle images in comparison with manual grading of images, with gonioscopy as the reference standard. METHODS. In this hospital-based, prospective study, subjects underwent gonioscopy by a single observer, and EyeCam imaging by a different operator. The anterior chamber angle in a quadrant was classified as closed if the posterior trabecular meshwork could not be seen. An eye was classified as having angle closure if there were two or more quadrants of closure. Automated grading of the angle images was performed using customized software. Agreement between the methods was ascertained by j statistic and comparison of area under receiver operating characteristic curves (AUC). RESULTS. One hundred forty subjects (140 eyes) were included, most of whom were Chinese (102/140, 72.9%) and women (72/140, 51.5%). Angle closure was detected in 61 eyes (43.6%) with gonioscopy in comparison with 59 eyes (42.1%, P ¼ 0.73) using manual grading, and 67 eyes (47.9%, P ¼ 0. CONCLUSIONS. Customized software for automated grading of EyeCam angle images was found to have good agreement with gonioscopy. Human observation of the EyeCam images may still be needed to avoid gross misclassification, especially in eyes with extensive angle closure

DeshadowGAN: a deep learning approach to remove shadows from optical coherence tomography images

Purpose: To remove blood vessel shadows from optical coherence tomography (OCT) images of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device for both eyes of 13 subjects. A custom generative adversarial network (named DeshadowGAN) was designed and trained with 2328 B-scans in order to remove blood vessel shadows in unseen B-scans. Image quality was assessed qualitatively (for artifacts) and quantitatively using the intralayer contrast—a measure of shadow visibility ranging from 0 (shadow-free) to 1 (strong shadow). This was computed in the retinal nerve fiber layer (RNFL), the inner plexiform layer (IPL), the photoreceptor (PR) layer, and the retinal pigment epithelium (RPE) layer. The performance of DeshadowGAN was also compared with that of compensation, the standard for shadow removal. Results: DeshadowGAN decreased the intralayer contrast in all tissue layers. On average, the intralayer contrast decreased by 33.7 ± 6.81%, 28.8 ± 10.4%, 35.9 ± 13.0%, and 43.0 ± 19.5% for the RNFL, IPL, PR layer, and RPE layer, respectively, indicating successful shadow removal across all depths. Output images were also free from artifacts commonly observed with compensation. Conclusions: DeshadowGAN significantly corrected blood vessel shadows in OCT images of the ONH. Our algorithm may be considered as a preprocessing step to improve the performance of a wide range of algorithms including those currently being used for OCT segmentation, denoising, and classification. Translational Relevance: DeshadowGAN could be integrated to existing OCT devices to improve the diagnosis and prognosis of ocular pathologies

In vivo measurements of prelamina and lamina cribrosa biomechanical properties in humans

Purpose: To develop and use a custom virtual fields method (VFM) to assess the biomechanical properties of human prelamina and lamina cribrosa (LC) in vivo. Methods: Clinical data of 20 healthy, 20 ocular hypertensive (OHT), 20 primary open-angle glaucoma, and 16 primary angle-closure glaucoma eyes were analyzed. For each eye, the intraocular pressure (IOP) and optical coherence tomography (OCT) images of the optic nerve head (ONH) were acquired at the normal state and after acute IOP elevation. The IOP-induced deformation of the ONH was obtained from the OCT volumes using a three-dimensional tracking algorithm and fed into the VFM to extract the biomechanical properties of the prelamina and the LC in vivo. Statistical measurements and P values from the Mann-Whitney-Wilcoxon tests were reported. Results: The average shear moduli of the prelamina and the LC were 64.2 ± 36.1 kPa and 73.1 ± 46.9 kPa, respectively. The shear moduli of the prelamina of healthy subjects were significantly lower than those of the OHT subjects. Comparisons between healthy and glaucoma subjects could not be made robustly due to a small sample size. Conclusions: We have developed a methodology to assess the biomechanical properties of human ONH tissues in vivo and provide preliminary comparisons in healthy and OHT subjects. Our proposed methodology may be of interest for glaucoma management

Evaluation of Choroidal Thickness, Intraocular Pressure, and Serum Osmolality After the Water Drinking Test in Eyes With Primary Angle Closure

PURPOSE. We evaluated changes in choroidal thickness (ChT), IOP, ocular biometry, and serum osmolality after the water drinking test (WDT) in subjects with previous acute primary angle closure (APAC) and primary angle closure glaucoma (PACG). METHODS. We evaluated 38 subjects, including 21 with APAC and 17 with PACG. Each subject underwent IOP measurement, A-scan biometry, spectral domain-optical coherence tomography (SDOCT), anterior segment-optical coherence tomography (ASOCT), and osmolality measurements at baseline, 30, and 60 minutes after consuming at least 10 mL/kg of water. The ChT at the macula was measured from SDOCT images using the 7-line scan protocol. The fellow-eyes of APAC (FE-APAC) were compared to eyes with PACG. RESULTS. The mean age 6 SD of the study subjects was 62.8 6 8.6 years and 21 (55.3%) were females. At baseline, serum osmolality was significantly lower (P < 0.001) in the FE-APAC group, whereas ChT was similar in both groups (P ¼ 0.56). At 30 minutes after WDT, both groups demonstrated a significant increase in IOP (FE-APAC, 3.0 [95% confidence interval {CI}, 1.52, 4.48] mm Hg; PACG, 5.06 [95% CI, 3.68, CONCLUSIONS. The increase in IOP after WDT was higher in PACG eyes compared to FE-APAC; however, the latter had lower serum osmolality at baseline. Change in mean ChT following WDT was associated with a lower baseline serum osmolality

Recent Advances in Breast Cancer Diagnosis Entering an Era of Precision Medicine

This article will cover some of the most recent advances in the diagnosis of the world’s most common cancer in women, namely, breast cancer as we enter the era of precision medicine. The authors will discuss the differences between East and West pertaining to the incidence and mortality rates, the types of breast cancer and the revised staging criteria of breast cancer according to the American Joint Committee on Cancer (AJCC) Staging Manual, 8th edition. In addition, the advances of newer imaging modalities are presented and compared with traditional ultrasonography and mammography