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1. Proper Orthogonal Decomposition Reduced Order Model for Tear Film Flows

   https://doi.org/10.23919/ACC50511.2021.9483018  

   Sahyoun, Samir; Wilson, Dan; Djouadi, Seddik M.; Wise, Steven M.; Abderrahmane, Hamid Ait (May 2021, 2021 American Control Conference (ACC))

   null (Ed.)

   Tear film plays a key role in protecting the cornea surface against contaminations and dry eye syndrome which can lead to symptoms of discomfort, visual trouble, and tear film instability with the potential to damage the ocular surface. In this paper, coupled nonlinear partial differential equations of the fourth order proposed by Aydemir et al. to describe the evolution of tear film dynamics are considered. These equations are of Benney type and known to suffer from unbounded behavior and lack of a global attractor. The objective here is to identify a reduced order modeling framework with the potential to be used as a basis for control in future work using smart tears with a surfactant that can modify the surface tension to prevent tear film breakup. Since the dynamics are infinite dimensional and nonlinear, a reduced order model based on the proper orthogonal decomposition (POD) is developed, analyzed, and compared to the full order model. Numerical simulations illustrate that only a small number of POD modes are required to accurately capture the tear film dynamics allowing for the full partial differential model to be represented as a low-dimensional set of coupled ordinary differential equations. 

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2. Mechanistic determination of tear film thinning via fitting simplified models to tear breakup

   https://doi.org/10.35119/maio.v3i1.114  

   Luke, Rayanne; Braun, Richard; Begley, Carolyn (September 2021, Modeling and Artificial Intelligence in Ophthalmology)

   Purpose: Little quantitative or mechanistic information about tear film breakup can be determined directly via current imaging techniques. In this paper, we present simplified mathematical models based on two proposed mechanisms of tear film breakup: evaporation of water from the tear film and tangential fluid flow within the tear film. We use our models to determine whether one or a combination of the two mechanisms causes tear film breakup in a variety of instances. In this study, we estimate related breakup parameters that cannot currently be measured in breakup during subject trials, such as tear film osmolarity and thinning rates. The present study validates our procedure against previous work.Methods: Five ordinary differential equation models for tear film thinning were designed that model evaporation, osmosis, and various types of tangential flow. Eight tear film breakup instances occurring within a time interval of 1–8 s postblink of five healthy subjects thatwere identified in fluorescence images in previous work were fit with these five models. The fitting procedure used a nonlinear least squares optimization that minimized the difference of the computed theoretical fluorescent intensity from the models and the experimental fluorescent intensity from the images. The optimization was conducted over the evaporation rate and up to three tangential flow rate parameters. The smallest norm of the difference was determined to correspond to the model that best explained the tear film dynamics.Results: All of the breakup instances were best fit by models with time-dependent tangential flow. Our optimal parameter values and thinning rate as well as tangential fluid flow profiles compare well with previous partial differential equation model results in most instances.Conclusion: Our fitting results suggest that a combination of tangential fluid flow and evaporation cause most of the breakup instances. Comparison with results from previous work suggests that the simplified models can capture the essential tear film dynamics in most cases, thereby validating this procedure for wider usage. 

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3. Virtual Staining of Defocused Autofluorescence Images of Unlabeled Tissue Using Deep Neural Networks

   https://doi.org/10.34133/2022/9818965  

   Zhang, Yijie; Huang, Luzhe; Liu, Tairan; Cheng, Keyi; de Haan, Kevin; Li, Yuzhu; Bai, Bijie; Ozcan, Aydogan (October 2022, Intelligent Computing)

   Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming, labor-intensive, and destructive to tissue. Standard virtual staining requires high autofocusing precision during the whole slide imaging of label-free tissue, which consumes a significant portion of the total imaging time and can lead to tissue photodamage. Here, we introduce a fast virtual staining framework that can stain defocused autofluorescence images of unlabeled tissue, achieving equivalent performance to virtual staining of in-focus label-free images, also saving significant imaging time by lowering the microscope’s autofocusing precision. This framework incorporates a virtual autofocusing neural network to digitally refocus the defocused images and then transforms the refocused images into virtually stained images using a successive network. These cascaded networks form a collaborative inference scheme: the virtual staining model regularizes the virtual autofocusing network through a style loss during the training. To demonstrate the efficacy of this framework, we trained and blindly tested these networks using human lung tissue. Using 4× fewer focus points with 2× lower focusing precision, we successfully transformed the coarsely-focused autofluorescence images into high-quality virtually stained H&E images, matching the standard virtual staining framework that used finely-focused autofluorescence input images. Without sacrificing the staining quality, this framework decreases the total image acquisition time needed for virtual staining of a label-free whole-slide image (WSI) by ~32%, together with a ~89% decrease in the autofocusing time, and has the potential to eliminate the laborious and costly histochemical staining process in pathology. 

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4. How Eye Blink Affects the Recognition Performance of Traditional Off-Angle Iris Images

   https://doi.org/10.1109/SoutheastCon52093.2024.10500074  

   Headley, Eli; Tebor, Daniel; Karakaya, Mahmut (March 2024, IEEE)

   Iris recognition is known to be a robust biometric method, leveraging distinctive iris patterns for reliable identification. However, challenges arise in traditional approaches, especially in non-ideal images captured in environments where subjects can freely move around. This research focuses on the impact of eyelid occlusion on off-angle iris recognition within a traditional framework. The primary objectives are to i) assess the impact of eye blink on overall recognition performance; ii) explore the effects on each specific angle; and iii) examine the occlusion level required to maintain an acceptable recognition performance in a traditional iris recognition pipeline. To generate different levels of eyelid occlusion, we manipulated the eyelid segmentation results to mimic eye blinking due to lack of adequate amount of eye blinking images in publicly available datasets. We conducted three sets of experiments using frontal and off-angle iris images from 100 different subjects. Based on the results, up to a certain occlusion level (60%), eyelid occlusion enhances performance for severe off-angle images by masking distortion-prone iris portions. This improvement comes from the masking of non-ideal portions of the off-angle iris images that are distorted by the refraction of light through the cornea caused by the gaze angle. This insight offers potential improvements for traditional iris recognition, highlighting the nuanced interplay of occlusion and gaze angle on recognition performance. 

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5. Texture Feature Extraction From Free-Viewing Scan Paths Using Gabor Filters With Downsampling

   https://doi.org/10.1145/3379157.3391423  

   Griffith, Henry K.; Komogortsev, Oleg V. (January 2020, ACM Symposium on Eye Tracking Research and Applications)

   null (Ed.)

   Texture-based features computed on eye movement scan paths have recently been proposed for eye movement biometric applications. Feature vectors were extracted within this prior work by computing the mean and standard deviation of the resulting images obtained through application of a Gabor filter bank. This paper describes preliminary work exploring an alternative technique for extracting features from Gabor filtered scan path images. Namely, features vectors are obtained by downsampling the filtered images, thereby retaining structured spatial information within the feature vector. The proposed technique is validated at various downsampling scales for data collected from 94 subjects during free-viewing of a fantasy movie trailer. The approach is demonstrated to reduce EER versus the previously proposed statistical summary technique by 11.7% for the best evaluated downsampling parameter. 

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