A major contributor to permanent vision loss is glaucoma. Early diagnosis is crucial for preventing vision loss due to glaucoma, making glaucoma screening essential. A more affordable method of glaucoma screening can be achieved by applying artificial intelligence to evaluate color fundus photographs (CFPs). We present the Justified Referral in AI Glaucoma Screening (JustRAIGS) challenge to further develop these AI algorithms for glaucoma screening and to assess their efficacy. To support this challenge, we have generated a distinctive big dataset containing more than 110,000 meticulously labeled CFPs obtained from approximately 60,000 patients and 500 distinct screening centers in the USA. Our objective is to assess the practicality of creating advanced and dependable AI systems that can take a CFP as input and produce the probability of referable glaucoma, as well as outputs for glaucoma justification by integrating both binary and multi-label classification tasks. This paper presents the evaluation of solutions provided by nine teams, recognizing the team with the highest level of performance. The highest achieved score of sensitivity at a specificity level of 95% was 85%, and the highest achieved score of Hamming losses average was 0.13. Additionally, we test the top three participants' algorithms on an external dataset to validate the performance and generalization of these models. The outcomes of this research can offer valuable insights into the development of intelligent systems for detecting glaucoma. Ultimately, findings can aid in the early detection and treatment of glaucoma patients, hence decreasing preventable vision impairment and blindness caused by glaucoma.

Single-cell RNA sequencing (scRNA-seq) provides a high throughput, quantitative and unbiased framework for scientists in many research fields to identify and characterize cell types within heterogeneous cell populations from various tissues. However, scRNA-seq based identification of discrete cell-types is still labor intensive and depends on prior molecular knowledge. Artificial intelligence has provided faster, more accurate, and user-friendly approaches for cell-type identification. In this review, we discuss recent advances in cell-type identification methods using artificial intelligence techniques based on single-cell and single-nucleus RNA sequencing data in vision science. The main purpose of this review paper is to assist vision scientists not only to select suitable datasets for their problems, but also to be aware of the appropriate computational tools to perform their analysis. Developing novel methods for scRNA-seq data analysis remains to be addressed in future studies.

MOTIVATION: To develop and assess the accuracy of deep learning models that identify different retinal cell types, as well as different retinal ganglion cell (RGC) subtypes, based on patterns of single-cell RNA sequencing (scRNA-seq) in multiple datasets. RESULTS: Deep domain adaptation models were developed and tested using three different datasets. The first dataset included 44 808 single retinal cells from mice (39 cell types) with 24 658 genes, the second dataset included 6225 single RGCs from mice (41 subtypes) with 13 616 genes and the third dataset included 35 699 single RGCs from mice (45 subtypes) with 18 222 genes. We used four loss functions in the learning process to align the source and target distributions, reduce misclassification errors and maximize robustness. Models were evaluated based on classification accuracy and confusion matrix. The accuracy of the model for correctly classifying 39 different retinal cell types in the first dataset was ∼92%. Accuracy in the second and third datasets reached ∼97% and 97% in correctly classifying 40 and 45 different RGCs subtypes, respectively. Across a range of seven different batches in the first dataset, the accuracy of the lead model ranged from 74% to nearly 100%. The lead model provided high accuracy in identifying retinal cell types and RGC subtypes based on scRNA-seq data. The performance was reasonable based on data from different batches as well. The validated model could be readily applied to scRNA-seq data to identify different retinal cell types and subtypes. AVAILABILITY AND IMPLEMENTATION: The code and datasets are available on https://github.com/DM2LL/Detecting-Retinal-Cell-Classes-and-Ganglion-Cell-Subtypes. We have also added the class labels of all samples to the datasets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Glaucoma is a complex eye disorder characterized by an optic neuropathy usually leading to typical patterns of structural and functional loss. Current classification of glaucoma damage is predominantly subjective and qualitative. Determining precise glaucoma-induced patterns of structural and functional loss is clinically significant because different patterns of loss could differentially impact patient quality of life. Here, we develop and apply deep archetypal analysis (DAA) to over 2,500 samples of optical coherence tomography (OCT) images around the optic disc of about 278 eyes with glaucoma to discover patterns of structural loss. We show that deep DAA is an appropriate approach for discovering patterns on the convex hull that encloses data points in a high-dimensional space, and that this approach is resistant to outliers. We also present a novel visualization with potential utility in clinical applications for assessing structural damage in patients with glaucoma. Compared to classical archetypal matrix decomposition, DAA discovers outlier-resistant patterns. Unlike deep learning models, DAA generates interpretable outcomes with clinical relevance. Finally, 16 discovered patterns of RNFL loss are visualized and clinically validated by glaucoma experts. Such patterns may serve as basic elements to quantify high-dimensional RNFL data in different applications.

Conventionally, glaucoma is diagnosed on the basis of visual field sensitivity (VF). However, the VF test is time-consuming, costly, and noisy. Using retinal thickness (RT) for glaucoma diagnosis is currently desirable. Thus, we propose a new methodology for estimating VF from RT in glaucomatous eyes. The key ideas are to use our new methods of pattern-based regularization (PBR) and pattern-based visualization (PBV) with convolutional neural networks (CNNs). PBR effectively conducts supervised learning of RT-VF relations in combination with unsupervised learning from non-paired VF data. We can thereby avoid overfitting of a CNN to small sized data. PBV visualizes functional correspondence between RT and VF with its nonlinearity preserved. We empirically demonstrate with real datasets that a CNN with PBR achieves the highest estimation accuracy to date and that a CNN with PBV is effective for knowledge discovery in an ophthalmological context.

In this paper, cooperative localization of mobile nodes in non-line of sight (NLOS) situation is considered using a constrained square root unscented Kalman filter (CSRUKF). The NLOS measurements are used as quadratic constraints, which form a convex feasible region inside which the positions of the mobile nodes are supposed to be. The CSRUKF consists of two main stages: square root unscented Kalman filter (SRUKF) and sigma point projection. In the former, a conventional SRUKF is used to estimate the state vector and the Cholesky factor of the error covariance matrix. In the latter, a new set of sigma points are generated, and the ones violating the constraints are projected onto the feasible region by solving a set of convex quadratically constrained quadratic programs (QCQP). Each QCQP can be solved independently and in parallel for each sigma point violating the constraint, thus the algorithm is suitable for distributed processing. The simulation results show that our algorithm can perform well in different NLOS scenarios.

In this paper, the properties of a new class of causal Markov random fields, named symmetric Markov mesh random field, are initially discussed. It is shown that the symmetric Markov mesh random fields from the upper corners are equivalent to the symmetric Markov mesh random fields from the lower corners. Based on this new random field, a symmetric, corner-independent, and isotropic image model is then derived which incorporates the dependency of a pixel on all its neighbors. The introduced image model comprises the product of several local 1D density and 2D joint density functions of pixels in an image thus making it computationally tractable and practically feasible by allowing the use of histogram and joint histogram approximations to estimate the model parameters. An image restoration application is also presented to confirm the effectiveness of the model developed. The experimental results demonstrate that this new model provides an improved tool for image modeling purposes compared to the conventional Markov random field models.

Stochastic image modeling based on conventional Markov random fields is extensively discussed in the literature. A new stochastic image model based on Markov random fields is introduced in this paper which overcomes the shortcomings of the conventional models easing the computation of the joint density function of images. As an application, this model is used to generate texture patterns. The lower computational complexity and easily controllable parameters of the model makes it a more useful model as compared to the conventional Markov random field-based models.

Auto-focusing is a key feature of modern camera systems that ensures a focused image is captured without the need for any user intervention. Passive auto-focusing is achieved based on a measure of sharpness or a sharpness function that is extracted from a series of images at different focal positions. With the rapid growth of stereo camera systems for 3D applications, this paper introduces a sharpness function when a series of stereo image pairs are captured at different focal positions. This function is defined based on the joint information between the left and right images. A comparison is carried out between the introduced sharpness function and the conventional sharpness functions when considering the left and right images separately in terms of sharpness accuracy and computation time.

This paper presents a new framework for facial expression recognition based on diffeomorphic matching. First landmarks are selected based on a manual or automatic method. All of the landmarks from different images are registered to a reference landmark set using a rigid registration algorithm. The pair-wise geodesic distance between all sets of landmarks are then computed using diffeomorphic matching. Finally, a K-Nearest Neighbor classifier (KNN) is used to classify a query image using the geodesic distances. Both the classification and classical MultiDimensional Scaling results show that geodesic distance is more effective than Euclidean distance on capturing the face shape variation.

In this letter, the linear predictability of discrete-time stationary stochastic processes with 1/|f|α-shaped power spectral density (PSD) is considered. In particular, the spectral flatness measure (SFM)-which yields a lower bound for the normalized mean-squared-error (NMSE) of any linear one-step-ahead (OSA) predictor-is obtained analytically as a function of α ∈ [0, 1]. By comparing the SFM bound to the NMSE of thep-tap linear minimum-mean-square error (LMMSE) predictor, it is shown that close to optimal NMSE performance may be achieved for relatively moderate values ofp. The performance of the LMMSE predictor for the discrete-time fractional Gaussian noise (DFGN), which may be viewed as the conventional discrete-time counterpart of continuous-time processes with 1/|f|α-shaped PSD, shows that the DFGN is more easily predicted than the discrete-time processes considered herein.

Image registration is a key component in existing magnetic resonance image processing software packages. Because of nonlinear variability in brain substructures, the commonly used registration pipelines applied to the entire brain area may not lead to accurate registration for substructures. This paper presents a two-stage registration approach for a better alignment of brain substructures or regions-of-interest. In the first stage, an affine transformation function is applied to the entire brain area and in the second stage, a nonrigid or deformable transformation function is applied to the substructure of interest. To show the usefulness of this two-stage registration, images from the Brainweb database are examined using normalized mutual information is used to assess the degree of alignment. The comparison results indicate improvements over the commonly used registration approaches.

This paper presents a lossless data hiding method using coefficients of integer wavelet domain. The modification of selected small coefficients of the high frequency subbands are used to embed data. We use the histogram modification to intelligently select the proper coefficients for data hiding. Data embedding is done by processing these selected coefficients. We show that at low payload data hiding our method has comparable PSNR than the best known reversible data hiding techniques, while at higher payloads it has significant superiority on image quality.