Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging

Optical Coherence Tomography (OCT) is one of the most important retinal imaging technique. However, involuntary motion artifacts still pose a major challenge in OCT imaging that compromises the quality of downstream analysis, such as retinal layer segmentation and OCT Angiography. We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single volumetric scan. The proposed method consists of two fully-convolutional neural networks that predict Z and X dimensional displacement maps sequentially in two stages. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods. Specifically, the method can recover the overall curvature of the retina, and can be generalized well to various diseases and resolutions

Glaucoma secundário à iridociclite heterocrômica de Fuchs

A iridociclite heterocrômica de Fuchs ou Síndrome de Fuchs é um tipo de uveíte relativamente incomum. Afeta igualmente ambos os sexos, na faixa etária dos 20-45 anos, tendo no quadro clássico uma inflamação não granulomatosa crônica unilateral na úvea anterior, de início insidioso, baixo grau de atividade, e não sendo responsiva aos corticóides. Normalmente tem um bom prognóstico, exceto quando ocorre o desenvolvimento de catarata e glaucoma, patologias que podem estar associadas à síndrome. Nesse caso, temos um paciente masculino, de 68 anos, que teve como primeira manifestação da síndrome o glaucoma

Learning to Correct Axial Motion in Oct for 3D Retinal Imaging

Optical Coherence Tomography (OCT) is a powerful technique for non-invasive 3D imaging of biological tissues at high resolution that has revolutionized retinal imaging. A major challenge in OCT imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose a convolutional neural network that learns to correct axial motion in OCT based on a single volumetric scan. The proposed method is able to correct large motion, while preserving the overall curvature of the retina. The experimental results show significant improvements in visual quality as well as overall error compared to the conventional methods in both normal and disease cases

Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks

Multi-modal retinal image registration plays an important role in the ophthalmological diagnosis process. The conventional methods lack robustness in aligning multi-modal images of various imaging qualities. Deep-learning methods have not been widely developed for this task, especially for the coarse-to-fine registration pipeline. To handle this task, we propose a two-step method based on deep convolutional networks, including a coarse alignment step and a fine alignment step. In the coarse alignment step, a global registration matrix is estimated by three sequentially connected networks for vessel segmentation, feature detection and description, and outlier rejection, respectively. In the fine alignment step, a deformable registration network is set up to find pixel-wise correspondence between a target image and a coarsely aligned image from the previous step to further improve the alignment accuracy. Particularly, an unsupervised learning framework is proposed to handle the difficulties of inconsistent modalities and lack of labeled training data for the fine alignment step. The proposed framework first changes multi-modal images into a same modality through modality transformers, and then adopts photometric consistency loss and smoothness loss to train the deformable registration network. The experimental results show that the proposed method achieves state-of-the-art results in Dice metrics and is more robust in challenging cases

Minimizing Iridium Oxide Electrodes for High Visual Acuity Subretinal Stimulation.

Vision loss from diseases of the outer retina, such as age-related macular degeneration, is among the leading causes of irreversible blindness in the world today. The goal of retinal prosthetics is to replace the photo-sensing function of photoreceptors lost in these diseases with optoelectronic hardware to electrically stimulate patterns of retinal activity corresponding to vision. To enable high-resolution retinal prosthetics, the scale of stimulating electrodes must be significantly decreased from current designs; however, this reduces the amount of stimulating current that can be delivered. The efficacy of subretinal stimulation at electrode sizes suitable for high visual acuity retinal prosthesis are not well understood, particularly within the safe charge injection limits of electrode materials. Here, we measure retinal ganglion cell (RGC) responses in a mouse model of blindness to evaluate the stimulation efficacy of 10, 20, and 30 μm diameter iridium oxide electrodes within the electrode charge injection limits, focusing on measures of charge threshold and dynamic range. Stimulation thresholds were lower for smaller electrodes, but larger electrodes could elicit a greater dynamic range of spikes and recruited more ganglion cells within charge injection limits. These findings suggest a practical lower limit for planar electrode size and indicate strategies for maximizing stimulation thresholds and dynamic range

Effective treatment of retinal neovascular leakage with fusogenic porous silicon nanoparticles delivering VEGF-siRNA.

Aim: To evaluate an intravitreally injected nanoparticle platform designed to deliver VEGF-A siRNA to inhibit retinal neovascular leakage as a new treatment for proliferative diabetic retinopathy and diabetic macular edema. Materials & methods: Fusogenic lipid-coated porous silicon nanoparticles loaded with VEGF-A siRNA, and pendant neovascular integrin-homing iRGD, were evaluated for efficacy by intravitreal injection in a rabbit model of retinal neovascularization. Results: For 12 weeks post-treatment, a reduction in vascular leakage was observed for treated diseased eyes versus control eyes (p = 0.0137), with a corresponding reduction in vitreous VEGF-A. Conclusion: Fusogenic lipid-coated porous silicon nanoparticles siRNA delivery provides persistent knockdown of VEGF-A and reduced leakage in a rabbit model of retinal neovascularization as a potential new intraocular therapeutic

Ultra-wide field and new wide field composite retinal image registration with AI-enabled pipeline and 3D distortion correction algorithm

PurposeThis study aimed to compare a new Artificial Intelligence (AI) method to conventional mathematical warping in accurately overlaying peripheral retinal vessels from two different imaging devices: confocal scanning laser ophthalmoscope (cSLO) wide-field images and SLO ultra-wide field images.MethodsImages were captured using the Heidelberg Spectralis 55-degree field-of-view and Optos ultra-wide field. The conventional mathematical warping was performed using Random Sample Consensus-Sample and Consensus sets (RANSAC-SC). This was compared to an AI alignment algorithm based on a one-way forward registration procedure consisting of full Convolutional Neural Networks (CNNs) with Outlier Rejection (OR CNN), as well as an iterative 3D camera pose optimization process (OR CNN + Distortion Correction [DC]). Images were provided in a checkerboard pattern, and peripheral vessels were graded in four quadrants based on alignment to the adjacent box.ResultsA total of 660 boxes were analysed from 55 eyes. Dice scores were compared between the three methods (RANSAC-SC/OR CNN/OR CNN + DC): 0.3341/0.4665/4784 for fold 1-2 and 0.3315/0.4494/4596 for fold 2-1 in composite images. The images composed using the OR CNN + DC have a median rating of 4 (out of 5) versus 2 using RANSAC-SC. The odds of getting a higher grading level are 4.8 times higher using our OR CNN + DC than RANSAC-SC (p < 0.0001).ConclusionPeripheral retinal vessel alignment performed better using our AI algorithm than RANSAC-SC. This may help improve co-localizing retinal anatomy and pathology with our algorithm

Artificial Intelligence for Automated Overlay of Fundus Camera and Scanning Laser Ophthalmoscope Images.

PurposeThe purpose of this study was to evaluate the ability to align two types of retinal images taken on different platforms; color fundus (CF) photographs and infrared scanning laser ophthalmoscope (IR SLO) images using mathematical warping and artificial intelligence (AI).MethodsWe collected 109 matched pairs of CF and IR SLO images. An AI algorithm utilizing two separate networks was developed. A style transfer network (STN) was used to segment vessel structures. A registration network was used to align the segmented images to each. Neither network used a ground truth dataset. A conventional image warping algorithm was used as a control. Software displayed image pairs as a 5 × 5 checkerboard grid composed of alternating subimages. This technique permitted vessel alignment determination by human observers and 5 masked graders evaluated alignment by the AI and conventional warping in 25 fields for each image.ResultsOur new AI method was superior to conventional warping at generating vessel alignment as judged by masked human graders (P < 0.0001). The average number of good/excellent matches increased from 90.5% to 94.4% with AI method.ConclusionsAI permitted a more accurate overlay of CF and IR SLO images than conventional mathematical warping. This is a first step toward developing an AI that could allow overlay of all types of fundus images by utilizing vascular landmarks.Translational relevanceThe ability to align and overlay imaging data from multiple instruments and manufacturers will permit better analysis of this complex data helping understand disease and predict treatment