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

Towards corneal structure mapping in the living eye using a combined X-ray scattering, biomedical imaging and machine learning approach

Corneal blindness is a leading cause of vision loss worldwide. Corneal curvature and transparency depend on the cornea’s unique collagen fibril architecture and, while much progress has been made in mapping collagen structure in cadaveric corneas, there is currently no way of imaging collagen fibril organization in the living eye. Our ultimate goal is to develop new artificial intelligence (AI)-assisted bioimaging technology to map collagen structure in live patient corneas. Here we present results from SPring-8 beamline BL40B2 in the form of wide-angle X-ray scattering (WAXS) maps of corneal structure from ex-vivo porcine eyes, which were previously scanned via optical coherence tomography (OCT), and will be used as a platform to train machine learning algorithms to “read” the equivalent in-vivo data from OCT images obtained from human patients

Proper Motions in the Galactic Bulge: Plaut's Window

A proper motion study of a field of 20' x 20' inside Plaut's low extinction window (l,b)=(0 deg,-8 deg), has been completed. Relative proper motions and photographic BV photometry have been derived for ~21,000 stars reaching to V~20.5 mag, based on the astrometric reduction of 43 photographic plates, spanning over 21 years of epoch difference. Proper motion errors are typically 1 mas/yr and field dependent systematics are below 0.2 mas/yr. Cross-referencing with the 2MASS catalog yielded a sample of ~8,700 stars, from which predominantly disk and bulge subsamples were selected photometrically from the JH color-magnitude diagram. The two samples exhibited different proper-motion distributions, with the disk displaying the expected reflex solar motion as a function of magnitude. Galactic rotation was also detected for stars between ~2 and ~3 kpc from us. The bulge sample, represented by red giants, has an intrinsic proper motion dispersion of (sigma_l,sigma_b)=(3.39, 2.91)+/-(0.11,0.09) mas/yr, which is in good agreement with previous results, and indicates a velocity anisotropy consistent with either rotational broadening or tri-axiality. A mean distance of 6.37^{+0.87}_{-0.77} kpc has been estimated for the bulge sample, based on the observed K magnitude of the horizontal branch red clump. The metallicity [M/H] distribution was also obtained for a subsample of 60 bulge giants stars, based on calibrated photometric indices. The observed [M/H] shows a peak value at [M/H]~-0.1 with an extended metal poor tail and around 30% of the stars with supersolar metallicity. No change in proper motion dispersion was observed as a function of [M/H]. We are currently in the process of obtaining CCD UBVRI photometry for the entire proper-motion sample of ~21,000 stars.Comment: Submitted to AJ April 17th 2007. Accepted June 8th 2007. 45 pages, 14 figure

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

Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images

Background/Aims Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle-closure glaucoma. Method In this study, we developed a deep convolutional neural network (DCNN) for the localisation of the scleral spur; moreover, we introduced an information-rich segmentation approach for this localisation problem. An ensemble of DCNNs for the segmentation of AS structures (iris, corneosclera shell adn anterior chamber) was developed. Based on the results of two previous processes, an algorithm to automatically quantify clinically important measurements were created. 200 images from 58 patients (100 eyes) were used for testing. Results With limited training data, the DCNN was able to detect the scleral spur on unseen anterior segment optical coherence tomography (ASOCT) images as accurately as an experienced ophthalmologist on the given test dataset and simultaneously isolated the AS structures with a Dice coefficient of 95.7%. We then automatically extracted eight clinically relevant ASOCT measurements and proposed an automated quality check process that asserts the reliability of these measurements. When combined with an OCT machine capable of imaging multiple radial sections, the algorithms can provide a more complete objective assessment. The total segmentation and measurement time for a single scan is less than 2 s. Conclusion This is an essential step towards providing a robust automated framework for reliable quantification of ASOCT scans, for applications in the diagnosis and management of angle-closure glaucoma