Adaptive Optics Retinal Imaging Research Articles

Active Cell Appearance Model Induced Generative Adversarial Networks for Annotation-Efficient Cell Segmentation and Identification on Adaptive Optics Retinal Images.

Data annotation is a fundamental precursor for establishing large training sets to effectively apply deep learning methods to medical image analysis. For cell segmentation, obtaining high quality annotations is an expensive process that usually requires manual grading by experts. This work introduces an approach to efficiently generate annotated images, called "A-GANs", created by combining an active cell appearance model (ACAM) with conditional generative adversarial networks (C-GANs). ACAM is a statistical model that captures a realistic range of cell characteristics and is used to ensure that the image statistics of generated cells are guided by real data. C-GANs utilize cell contours generated by ACAM to produce cells that match input contours. By pairing ACAM-generated contours with A-GANs-based generated images, high quality annotated images can be efficiently generated. Experimental results on adaptive optics (AO) retinal images showed that A-GANs robustly synthesize realistic, artificial images whose cell distributions are exquisitely specified by ACAM. The cell segmentation performance using as few as 64 manually-annotated real AO images combined with 248 artificially-generated images from A-GANs was similar to the case of using 248 manually-annotated real images alone (Dice coefficients of 88% for both). Finally, application to rare diseases in which images exhibit never-seen characteristics demonstrated improvements in cell segmentation without the need for incorporating manual annotations from these new retinal images. Overall, A-GANs introduce a methodology for generating high quality annotated data that statistically captures the characteristics of any desired dataset and can be used to more efficiently train deep-learning-based medical image analysis applications.

Automated Iterative Label Transfer Improves Segmentation of Noisy Cells in Adaptive Optics Retinal Images.

High quality data labeling is essential for improving the accuracy of deep learning applications in medical imaging. However, noisy images are not only under-represented in training datasets, but also, labeling of noisy data is low quality. Unfortunately, noisy images with poor quality labels are exacerbated by traditional data augmentation strategies. Real world images contain noise and can lead to unexpected drops in algorithm performance. In this paper, we present a non-traditional, purposeful data augmentation method to specifically transfer high quality automated labels into noisy image regions for incorporation into the training dataset. The overall approach is based on the use of paired images of the same cells in which variable image noise results in cell segmentation failures. Iteratively updating the cell segmentation model with accurate labels of noisy image areas resulted in an improvement in Dice coefficient from 77% to 86%. This was achieved by adding only 3.4% more cells to the training dataset, showing that local label transfer through graph matching is an effective augmentation strategy to improve segmentation.

Reduced foveal cone density in early idiopathic macular telangiectasia

ObjectiveIdiopathic macular telangiectasia (MacTel) is considered primarily a vascular disease affecting juxtafoveal retinal capillaries. However, recent evidence suggests that neuronal changes may occur early in disease development. We used high-resolution...

Proteostasis Modulation Prevents Photoreceptor Pathology in Retinal Organoids

Dysregulation of the endoplasmic reticulum (ER) Unfolded Protein Response (UPR) is implicated in the pathology of many human diseases associated with ER stress. Inactivating genetic variants in the UPR regulator Activating Transcription Factor 6 (ATF6) cause severe congenital heritable vision loss in patients by an unknown pathomechanism. To investigate this, we generated retinal organoids from patient iPSCs carrying ATF6 disease-causing variants and ATF6 null hESCs generated by CRISPR. Interestingly, we found that cone photoreceptor cells in ATF6 mutant retinal organoids lacked inner and outer segments concomitant with absence of cone phototransduction gene expression; while rod photoreceptors developed normally. Adaptive optics retinal imaging of patients with disease-causing variants in ATF6 also showed absence of cone inner/outer segment structures but preserved rod structures, mirroring the phenotypes observed in our retinal organoids. These results reveal that ATF6 is essential for the formation of human cone photoreceptors, and associated absence of cone phototransduction components explains the severe visual impairment in patients with ATF6 -associated retinopathy. Moreover, we show that a selective small molecule ATF6 activator compound restores the transcriptional activity of ATF6 disease-causing variants and stimulates the growth of cone photoreceptors in patient retinal organoids, demonstrating that pharmacologic targeting of ATF6 signaling is a therapeutic strategy that needs to be further explored for blinding retinal diseases.

An ADMM-LAP method for total variation myopic deconvolution of adaptive optics retinal images

Adaptive optics corrected flood imaging of the retina is a popular technique for studying the retinal structure and function in the living eye. However, the raw retinal images are usually of poor contrast and the interpretation of such images requires image deconvolution. Different from standard deconvolution problems where the point spread function (PSF) is completely known, the PSF in these retinal imaging problems is only partially known which leads to the more complicated myopic (mildly blind) deconvolution problem. In this paper, we propose an efficient numerical scheme for solving this myopic deconvolution problem with total variational (TV) regularization. First, we apply the alternating direction method of multipliers (ADMM) to tackle the TV regularizer. Specifically, we reformulate the TV problem as an equivalent equality constrained problem where the objective function is separable, and then minimize the augmented Lagrangian function by alternating between two (separated) blocks of unknowns to obtain the solution. Due to the structure of the retinal images, the subproblems with respect to the fidelity term appearing within each ADMM iteration are tightly coupled and a variation of the linearize and project method is designed to solve these subproblems efficiently. The proposed method is called the ADMM-LAP method. Theoretically, we establish the subsequence convergence of the ADMM-LAP method to a stationary point. Both the theoretical complexity analysis and numerical results are provided to demonstrate the efficiency of the ADMM-LAP method.

Carbonic anhydrase inhibition in X-linked retinoschisis: An eye on the photoreceptors

The retinoschisin protein is encoded on the short arm of the X-chromosome by RS1, is expressed abundantly in photoreceptor inner segments and in bipolar cells, and is secreted as an octamer that maintains the structural integrity of the retina. Mutations in RS1 lead to X-linked retinoschisis (XLRS), a disease characterized by the formation of cystic spaces between boys' retinal layers that frequently present in ophthalmoscopy as a “spoke-wheel” pattern on their maculae and by progressively worsening visual acuity (VA). There is no proven therapy for XLRS, but there is mixed evidence that carbonic anhydrase inhibitors (CAIs) produce multiple beneficial effects, including improved VA and decreased volume of cystic spaces. Consequently, linear mixed-effects (LME) models were used to evaluate the effects of CAI therapy on VA and central retinal thickness (CRT, a proxy for cystic cavity volume) in a review of 19 patients’ records. The mechanism of action of action of CAIs is unclear but, given that misplaced retinoschisin might accumulate in the photoreceptors, it is possible—perhaps even likely—that CAIs act to benefit the function of photoreceptors and the neighboring retinal pigment epithelium by acidification of the extracellular milieu; patients on CAIs have among the most robust photoreceptor responses. Therefore, a small subset of five subjects were recruited for imaging on a custom multimodal adaptive optics retinal imager for inspection of their parafoveal cone photoreceptors. Those cones that were visible, which numbered far fewer than in controls, were enlarged, consistent with the retinoschisin accumulation hypothesis. Results of the LME modeling found that there is an initial benefit to both VA and CRT in CAI therapy, but these wane, in both cases, after roughly two years. That said, even a short beneficial effect of CAIs on the volume of the cystic spaces may give CAI therapy an important role as pretreatment before (or immediately following) administration of gene therapy.

SPATIALLY INFORMED CNN FOR AUTOMATED CONE DETECTION IN ADAPTIVE OPTICS RETINAL IMAGES.

Adaptive optics (AO) scanning laser ophthalmoscopy offers cellular level in-vivo imaging of the human cone mosaic. Existing analysis of cone photoreceptor density in AO images require accurate identification of cone cells, which is a time and labor-intensive task. Recently, several methods have been introduced for automated cone detection in AO retinal images using convolutional neural networks (CNN). However, these approaches have been limited in their ability to correctly identify cones when applied to AO images originating from different locations in the retina, due to changes to the reflectance and arrangement of the cone mosaics with eccentricity. To address these limitations, we present an adapted CNN architecture that incorporates spatial information directly into the network. Our approach, inspired by conditional generative adversarial networks, embeds the retina location from which each AO image was acquired as part of the training. Using manual cone identification as ground truth, our evaluation shows general improvement over existing approaches when detecting cones in the middle and periphery regions of the retina, but decreased performance near the fovea.

Wide-vergence, multi-spectral adaptive optics scanning laser ophthalmoscope with diffraction-limited illumination and collection.

Visualizing and assessing the function of microscopic retinal structures in the human eye is a challenging task that has been greatly facilitated by ophthalmic adaptive optics (AO). Yet, as AO imaging systems advance in functionality by employing multiple spectral channels and larger vergence ranges, achieving optimal resolution and signal-to-noise ratios (SNR) becomes difficult and is often compromised. While current-generation AO retinal imaging systems have demonstrated excellent, near diffraction-limited imaging performance over wide vergence and spectral ranges, a full theoretical and experimental analysis of an AOSLO that includes both the light delivery and collection optics has not been done, and neither has the effects of extending wavefront correction from one wavelength to imaging performance in different spectral channels. Here, we report a methodology and system design for simultaneously achieving diffraction-limited performance in both the illumination and collection paths for a wide-vergence, multi-spectral AO scanning laser ophthalmoscope (SLO) over a 1.2 diopter vergence range while correcting the wavefront in a separate wavelength. To validate the design, an AOSLO was constructed to have three imaging channels spanning different wavelength ranges (543 ± 11 nm, 680 ± 11 nm, and 840 ± 6 nm, respectively) and one near-infrared wavefront sensing channel (940 ± 5 nm). The AOSLO optics and their alignment were determined via simulations in optical and optomechanical design software and then experimentally verified by measuring the AOSLO's illumination and collection point spread functions (PSF) for each channel using a phase retrieval technique. The collection efficiency was then measured for each channel as a function of confocal pinhole size when imaging a model eye achieving near-theoretical performance. Imaging results from healthy human adult volunteers demonstrate the system's ability to resolve the foveal cone mosaic in all three imaging channels despite a wide spectral separation between the wavefront sensing and imaging channels.

High-resolution iris and retinal imaging in multisystemic smooth muscle dysfunction syndrome due to a novel Asn117Lys substitution in ACTA2: a case report

BackgroundCongenital mydriasis and retinal arteriolar tortuosity are associated with the life-threatening multisystemic smooth muscle dysfunction syndrome (MSMDS) due to mutations in the gene, ACTA2, which encodes alpha-smooth muscle actin (α-SMA). Previous reports attributed MSMDS-related congenital mydriasis to the absence of iris sphincter muscle. Similarly, it has been hypothesized that abnormal proliferation of the vascular smooth muscle cells causes the marked tortuosity of retinal arterioles in MSMDS. In this report, high-resolution ocular imaging reveals unexpected findings that reject previous hypotheses.Case presentationThe proband is a 37-year-old female with a history of neonatal patent ductus arteriosus (PDA) ligation, left-sided choreiform movements at the age of 11 and a transient aphasia with right-sided weakness at the age of 30. Her older sister also had PDA ligation and congenital mydriasis but no neurological deficit up to age 41. Magnetic resonance angiogram demonstrated cerebrovascular lesions resembling but distinct from Moyamoya disease, characterised by internal carotid artery dilatation, terminal segment stenosis and absent basal collaterals. Their mother had poorly reactive pupils with asymptomatic cerebral arteriopathy resembling her daughters. All three had prominent retinal arteriolar tortuosity. The daughters were heterozygous and the mother was a somatic mosaic for a novel c.351C > G (p.Asn117Lys) transversion in ACTA2. Iris optical coherence tomography (OCT) showed a hyporeflective band anterior to the pigment epithelium indicating the presence of dysfunctional sphincter muscle. Adaptive optics retinal imaging showed no thickening of the arteriolar vessel wall whilst OCT angiography showed extreme corkscrew course of arterioles suggesting vessel elongation.ConclusionsIn addition to the known association between Met46, Arg179 and Arg258 substitutions and ACTA2-related arteriopathy, this case illustrates the possibility that Asn117 also plays an important role in α-SMA function within the cerebrovascular smooth muscle cell. MSMDS-related congenital mydriasis is due to reduced iris sphincter contractility rather than its absence. Retinal arteriolar tortuosity might be due to longitudinal proliferation of arteriolar smooth muscle cells. The described cerebrovascular and ocular signs are consistent with predicted effects of the novel Asn117Lys substitution in ACTA2.

Quality improvement of adaptive optics retinal images using conditional adversarial networks.

The adaptive optics (AO) technique is widely used to compensate for ocular aberrations and improve imaging resolution. However, when affected by intraocular scatter, speckle noise, and other factors, the quality of the retinal image will be degraded. To effectively improve the image quality without increasing the imaging system's complexity, the post-processing method of image deblurring is adopted. In this study, we proposed a conditional adversarial network-based method for directly learning an end-to-end mapping between blurry and restored AO retinal images. The proposed model was validated on synthetically generated AO retinal images and real retinal images. The restoration results of synthetic images were evaluated with the metrics of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), perceptual distance, and error rate of cone counting. Moreover, the blind image quality index (BIQI) was used as the no-reference image quality assessment (NR-IQA) algorithm to evaluate the restoration results on real AO retinal images. The experimental results indicate that the images restored by the proposed method have sharper quality and higher signal-to-noise ratio (SNR) when compared with other state-of-the-art methods, which has great practical significance for clinical research and analysis.

自适应光学视网膜成像系统的激光安全分析

自适应光学视网膜成像系统的激光安全分析

Eye tracking-based estimation and compensation of chromatic offsets for multi-wavelength retinal microstimulation with foveal cone precision.

Multi-wavelength ophthalmic imaging and stimulation of photoreceptor cells require consideration of chromatic dispersion of the eye, manifesting in longitudinal and transverse chromatic aberrations. Contemporary image-based techniques to measure and correct transverse chromatic aberration (TCA) and the resulting transverse chromatic offset (TCO) in an adaptive optics retinal imaging system are precise but lack compensation of small but significant shifts in eye position occurring during in vivo testing. Here, we present a method that requires only a single measurement of TCO during controlled movements of the eye to map retinal chromatic image shifts to the image space of a pupil camera. After such calibration, TCO can be compensated by continuously monitoring eye position during experimentation and by interpolating correction vectors from a linear fit to the calibration data. The average change rate of TCO per head shift and the correlation between Kappa and the individual foveal TCA are close to the expectations based on a chromatic eye model. Our solution enables continuous compensation of TCO with high spatial precision and avoids high light intensities required for re-measuring TCO after eye position changes, which is necessary for foveal cone-targeted psychophysical experimentation.

High-Resolution Retinal Imaging Reveals Preserved Cone Photoreceptor Density and Choroidal Thickness in Female Carriers of Choroideremia.

To characterize the photoreceptors and choroidal morphology of heterozygous female carriers of choroideremia who typically do not have any visual defects but can have severe funduscopic changes. This was a clinical case series study. Detailed ophthalmic examinations were performed on six female carriers from four families with choroideremia. The subfoveal choroidal thickness (SFCT) was determined by spectral-domain optical coherence tomography (SD-OCT) and the cone photoreceptor density by adaptive optics (AO) retinal imaging. SFCT and cone densities of the carriers were compared to that of normal eyes of healthy subjects. The mean age of the carriers was 42.5 years. Fundus photographs showed diffuse, patchy depigmentation; however, the SFCT was within the normal limits. AO retinal imaging revealed preserved cone densities at temporal eccentricities from 2 to 8 angular degrees. The findings indicate that despite the presence of distinctive depigmentation of the retinal pigment epithelium in female carriers of choroideremia, their cone photoreceptor densities and SFCT are well-preserved. These observations may account for the good visual acuity and lack of an awareness of visual disturbances. [Ophthalmic Surg Lasers Imaging Retina. 2019;50:76-85.].

Adaptive Optics Retinal Imaging in CNGA3-Associated Achromatopsia: Retinal Characterization, Interocular Symmetry, and Intrafamilial Variability.

PurposeTo investigate retinal structure in subjects with CNGA3-associated achromatopsia and evaluate disease symmetry and intrafamilial variability.MethodsThirty-eight molecularly confirmed subjects underwent ocular examination, optical coherence tomography (OCT), and nonconfocal split-detection adaptive optics scanning light ophthalmoscopy (AOSLO). OCT scans were used for evaluating foveal hypoplasia, grading foveal ellipsoid zone (EZ) disruption, and measuring outer nuclear layer (ONL) thickness. AOSLO images were used to quantify peak foveal cone density, intercell distance (ICD), and the coefficient of variation (CV) of ICD.ResultsMean (±SD) age was 25.9 (±13.1) years. Mean (± SD) best corrected visual acuity (BCVA) was 0.87 (±0.14) logarithm of the minimum angle of resolution. Examination with OCT showed variable disruption or loss of the EZ. Seven subjects were evaluated for disease symmetry, with peak foveal cone density, ICD, CV, ONL thickness, and BCVA not differing significantly between eyes. A cross-sectional evaluation of AOSLO imaging showed a mean (±SD) peak foveal cone density of 19,844 (±13,046) cones/mm2. There was a weak negative association between age and peak foveal cone density (r = −0.397, P = 0.102), as well as between EZ grade and age (P = 0.086).ConclusionsThe remnant cone mosaics were irregular and variably disrupted, with significantly lower peak foveal cone density than unaffected individuals. Variability was also seen among subjects with identical mutations. Therefore, subjects should be considered on an individual basis for stratification in clinical trials. Interocular symmetry suggests that both eyes have comparable therapeutic potential and the fellow eye can serve as a valid control. Longitudinal studies are needed, to further examine the weak negative association between age and foveal cone structure observed here.

Deriving Visual Cues from Deep Learning to Achieve Subpixel Cell Segmentation in Adaptive Optics Retinal Images.

Direct visualization of photoreceptor cells, specialized neurons in the eye that sense light, can be achieved using adaptive optics (AO) retinal imaging. Evaluating photoreceptor cell morphology in retinal diseases is important for monitoring the onset and progression of blindness, but segmentation of these cells is a critical first step. Most segmentation approaches focus on cell region extraction, without directly considering cell boundary localization. This makes it difficult to track cells that have ambiguous boundaries, which result from low image contrast, anisotropic cell regions, or densely-packed cells whose boundaries appear to touch each other. These are all characteristics of the AO images that we consider here. To address these challenges, we develop an AOSeg-Net method that uses a multi-channel U-Net to predict the spatial probabilities of the cell boundary and obtain cell centroid and region distribution information as a means for facilitating cell segmentation. Five-color theorem guarantees the separation of any touching cells. Finally, a region-based level set algorithm that combines all of these visual cues is used to achieve subpixel cell segmentation. Five-fold cross-validation on 428 high resolution retinal images from 23 human subjects showed that AOSegNet substantially outperformed the only other existing approach with Dice coefficients [%] of 84.7 and 78.4, respectively, and average symmetric contour distances [μm] of 0.59 and 0.80, respectively.

Active Appearance Model Induced Generative Adversarial Network for Controlled Data Augmentation.

Data augmentation is an important strategy for enlarging training datasets in deep learning-based medical image analysis. This is because large, annotated medical datasets are not only difficult and costly to generate, but also quickly become obsolete due to rapid advances in imaging technology. Image-to-image conditional generative adversarial networks (C-GAN) provide a potential solution for data augmentation. However, annotations used as inputs to C-GAN are typically based only on shape information, which can result in undesirable intensity distributions in the resulting artificially-created images. In this paper, we introduce an active cell appearance model (ACAM) that can measure statistical distributions of shape and intensity and use this ACAM model to guide C-GAN to generate more realistic images, which we call A-GAN. A-GAN provides an effective means for conveying anisotropic intensity information to C-GAN. A-GAN incorporates a statistical model (ACAM) to determine how transformations are applied for data augmentation. Traditional approaches for data augmentation that are based on arbitrary transformations might lead to unrealistic shape variations in an augmented dataset that are not representative of real data. A-GAN is designed to ameliorate this. To validate the effectiveness of using A-GAN for data augmentation, we assessed its performance on cell analysis in adaptive optics retinal imaging, which is a rapidly-changing medical imaging modality. Compared to C-GAN, A-GAN achieved stability in fewer iterations. The cell detection and segmentation accuracy when assisted by A-GAN augmentation was higher than that achieved with C-GAN. These findings demonstrate the potential for A-GAN to substantially improve existing data augmentation methods in medical image analysis.

Morphometric analysis of retinal arterioles in control and hypertensive population using adaptive optics imaging.

Purpose:To measure the wall-to-lumen ratio (WLR) and the vascular wall cross-sectional area (WCSA) of retinal arterioles by an Adaptive Optics (AO) retinal camera using semi-automated software and comparing them between control and hypertensive population.Methods:This was a cross-sectional observational study including a hypertensive group and a control group. Subjects were examined and their medical history recorded. Retinal arteriolar morphometry was assessed by rtx1 AO retinal camera using AOdetect Artery semiautomated software. Main Outcome Measures: WLR and WCSA were measured on the basis of retinal arteriolar wall thickness (W1, W2), lumen diameter (LD) and vessel diameter (VD). Influence of age and arterial hypertension on the WLR and WCSA were examined.Results:A total of 150 human subjects were included out of which 110 were controls and 40 were hypertensives under treatment. There was statistically significant difference in the age, systolic and diastolic blood pressures between the control and hypertensive groups (P < 0.01). We found no significant correlation between age and WLR (R2 = 0.049, P > 0.05) or age and WCSA (R2 = 0.045, P > 0.05). We observed a significant difference in WLR and WCSA measurements between control and hypertensive groups (P < 0.01). On measuring intra-observer variability (IOV) we found excellent consistency.Conclusion:AO retinal imaging allows a direct measurement of the retinal vessel wall and LD with excellent IOV. WLR and WCSA reflect the remodelling process and can be used to further aid the early detection and monitoring of systemic hypertension.

Cone Photoreceptor Cell Segmentation and Diameter Measurement on Adaptive Optics Images Using Circularly Constrained Active Contour Model.

PurposeCone photoreceptor cells can be noninvasively imaged in the living human eye by using nonconfocal adaptive optics scanning ophthalmoscopy split detection. Existing metrics, such as cone density and spacing, are based on simplifying cone photoreceptors to single points. The purposes of this study were to introduce a computer-aided approach for segmentation of cone photoreceptors, to apply this technique to create a normal database of cone diameters, and to demonstrate its use in the context of existing metrics.MethodsCone photoreceptor segmentation is achieved through a circularly constrained active contour model (CCACM). Circular templates and image gradients attract active contours toward cone photoreceptor boundaries. Automated segmentation from in vivo human subject data was compared to ground truth established by manual segmentation. Cone diameters computed from curated data (automated segmentation followed by manual removal of errors) were compared with histology and published data.ResultsOverall, there was good agreement between automated and manual segmentations and between diameter measurements (n = 5191 cones) and published histologic data across retinal eccentricities ranging from 1.35 to 6.35 mm (temporal). Interestingly, cone diameter was correlated to both cone density and cone spacing (negatively and positively, respectively; P < 0.01 for both). Application of the proposed automated segmentation to images from a patient with late-onset retinal degeneration revealed the presence of enlarged cones above individual reticular pseudodrusen (average 23.0% increase, P < 0.05).ConclusionsCCACM can accurately segment cone photoreceptors on split detection images across a range of eccentricities. Metrics derived from this automated segmentation of adaptive optics retinal images can provide new insights into retinal diseases.

Adaptive optics imaging of the human retina.

Adaptive Optics (AO) retinal imaging has provided revolutionary tools to scientists and clinicians for studying retinal structure and function in the living eye. From animal models to clinical patients, AO imaging is changing the way scientists are approaching the study of the retina. By providing cellular and subcellular details without the need for histology, it is now possible to perform large scale studies as well as to understand how an individual retina changes over time. Because AO retinal imaging is non-invasive and when performed with near-IR wavelengths both safe and easily tolerated by patients, it holds promise for being incorporated into clinical trials providing cell specific approaches to monitoring diseases and therapeutic interventions. AO is being used to enhance the ability of OCT, fluorescence imaging, and reflectance imaging. By incorporating imaging that is sensitive to differences in the scattering properties of retinal tissue, it is especially sensitive to disease, which can drastically impact retinal tissue properties. This review examines human AO retinal imaging with a concentration on the use of the Adaptive Optics Scanning Laser Ophthalmoscope (AOSLO). It first covers the background and the overall approaches to human AO retinal imaging, and the technology involved, and then concentrates on using AO retinal imaging to study the structure and function of the retina.

In Vivo Functional Imaging of Retinal Neurons Using Red and Green Fluorescent Calcium Indicators.

Adaptive optics retinal imaging of fluorescent calcium indicators is a minimally invasive method used to study retinal physiology over extended periods of time. It has potential for discovering novel retinal circuits, tracking retinal function in animal models of retinal disease, and assessing vision restoration therapy. We previously demonstrated functional adaptive optics imaging of retinal neurons in the living eye using green fluorescent calcium indicators; however, the use of green fluorescent indicators presents challenges that stem from the fact that they are excited by short-wavelength light. Using red fluorescent calcium indicators such as jRGECO1a, which is excited with longer-wavelength light (~560nm), makes imaging approximately five times safer than using short-wavelength light (~500nm) used to excite green fluorescent calcium indicators such as GCaMP6s. Red fluorescent indicators also provide alternative wavelength imaging regimes to overcome cross talk with the sensitivities of intrinsic photoreceptors and blue light-activated channelrhodopsins. Here we evaluate jRGECO1a for in vivo functional adaptive optics imaging of retinal neurons using single-photon excitation in mice. We find that jRGECO1a provides similar fidelity as the established green indicator GCaMP6s.