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Sensors in Vision Research and Ophthalmic Instrumentation

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (29 July 2022) | Viewed by 8776

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Special Issue Editor

Dr. Boris I. Gramatikov

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Guest Editor

The Wilmer Eye Institute, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Interests: electronics; optoelectronics; computers; computer modeling; signal/image processing; data analysis; instrumentation design; biophotonics; polarization-sensitive optics; ophthalmic and biomedical optics; all applied to the development of diagnostic methods and devices for ophthalmology and vision research

Special Issue Information

Dear Colleagues,

In the past few decades, the use of light has played an important role in revealing structural and functional information from the human eye in a nondestructive and non-invasive manner. Ophthalmic optics and related disciplines have been expanding steadily, providing scientists and doctors with priceless multidisciplinary information in addition to enabling new diagnostic and therapeutic methods. New scanning and imaging technologies have had a tremendous impact on ophthalmology, where information about the fovea and the optic nerve is essential. A number of ophthalmic diagnostic technologies have been developed and refined, such as scanning laser ophthalmoscopy, the adaptive optics scanning laser ophthalmoscopy, scanning laser polarimetry, optical coherence tomography (OCT), Doppler OCT, polarization-sensitive OCT, OCT angiography, fluorescein angiography, indocyanine green angiography, and imaging based on near-infrared reflectance, fundus autofluorescence, and photoacoustic ocular imaging, etc. In addition, the term “multi-modal imaging” has emerged and is being increasingly used to describe the approach of diagnosing a retinal condition by combining complementary imaging modalities for the purpose of diagnosis, classification, prognostication, monitoring, and management. Similarly, diagnostic decision making based on ophthalmic sensors has strongly been enhanced by the utilization of modern machine learning methods, especially deep learning, widely used for segmentation purposes. Numerous relevant technologies have emerged, such as liquid-crystal-based spatial light modulation, liquid crystal lenses, wavefront correction, Jones matrix OCT, birefringence and depolarization imaging, photoacoustic microscopy, etc., all holding promise for further improving the precision of sensors used in vision research and ophthalmic instrumentation.

Dr. Boris I. Gramatikov
Guest Editor

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Keywords

retinal imaging
polarization sensitive retinal scanning
optical coherence tomography (OCT)
optical coherence microscopy
OCT angiography
fluorescein angiography
scanning laser ophthalmoscopy
adaptive optics
retinal oximetry
handheld diagnostic devices
photoacoustic imaging
multimodal retinal imaging
fundus photography
low vision
retinal prosthesis
automatic segmentation
machine learning
deep learning
eye alignment and eye-tracking
clinical applications: diagnostic, guiding therapy, patient monitoring, disease prevention, and risk assessment
screening technologies

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Published Papers (3 papers)

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Research

17 pages, 2582 KiB

Open AccessArticle

Decision Trees for Glaucoma Screening Based on the Asymmetry of the Retinal Nerve Fiber Layer in Optical Coherence Tomography

by Rafael Berenguer-Vidal, Rafael Verdú-Monedero, Juan Morales-Sánchez, Inmaculada Sellés-Navarro, Oleksandr Kovalyk and José-Luis Sancho-Gómez

Sensors 2022, 22(13), 4842; https://doi.org/10.3390/s22134842 - 27 Jun 2022

Cited by 9 | Viewed by 2548

Abstract

Purpose: The aim of this study was to analyze the relevance of asymmetry features between both eyes of the same patient for glaucoma screening using optical coherence tomography. Methods: Spectral-domain optical coherence tomography was used to estimate the thickness of the peripapillary retinal nerve fiber layer in both eyes of the patients in the study. These measurements were collected in a dataset from healthy and glaucoma patients. Several metrics for asymmetry in the retinal nerve fiber layer thickness between the two eyes were then proposed. These metrics were evaluated using the dataset by performing a statistical analysis to assess their significance as relevant features in the diagnosis of glaucoma. Finally, the usefulness of these asymmetry features was demonstrated by designing supervised machine learning models that can be used for the early diagnosis of glaucoma. Results: Machine learning models were designed and optimized, specifically decision trees, based on the values of proposed asymmetry metrics. The use of these models on the dataset provided good classification of the patients (accuracy 88%, sensitivity 70%, specificity 93% and precision 75%). Conclusions: The obtained machine learning models based on retinal nerve fiber layer asymmetry are simple but effective methods which offer a good trade-off in classification of patients and simplicity. The fast binary classification relies on a few asymmetry values of the retinal nerve fiber layer thickness, allowing their use in the daily clinical practice for glaucoma screening. Full article

(This article belongs to the Special Issue Sensors in Vision Research and Ophthalmic Instrumentation)

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30 pages, 17779 KiB

Open AccessArticle

Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

by Rafael Berenguer-Vidal, Rafael Verdú-Monedero, Juan Morales-Sánchez, Inmaculada Sellés-Navarro, Rocío del Amor, Gabriel García and Valery Naranjo

Sensors 2021, 21(23), 8027; https://doi.org/10.3390/s21238027 - 1 Dec 2021

Cited by 9 | Viewed by 3476

Abstract

Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods. Full article

(This article belongs to the Special Issue Sensors in Vision Research and Ophthalmic Instrumentation)

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8 pages, 1316 KiB

Open AccessCommunication

Bluetooth Low Energy Beacon Sensors to Document Handheld Magnifier Use at Home by People with Low Vision

by Ava K. Bittner, Max Estabrook and Niki Dennis

Sensors 2021, 21(21), 7065; https://doi.org/10.3390/s21217065 - 25 Oct 2021

Viewed by 1858

Abstract

We explored the feasibility of using Bluetooth low energy (BLE) beacon sensors to determine when individuals with low vision (LV) use handheld magnifiers at home. Knowing the frequency and duration of magnifier use would be helpful to document increased magnifier use after successful rehabilitation training, or conversely, to know when someone has abandoned a magnifier and requires assistance. Estimote Sticker BLE beacon sensors were attached to the handles of optical handheld magnifiers and dispensed to eight LV subjects to use at home. Temperature and motion data from the BLE beacon sensors were collected every second by a custom mobile application on a nearby smartphone and transmitted to a secure database server. Subjects noted the date and start/end times of their magnifier use in a diary log. Each of the 99 diary-logged self-reports of magnifier use across subjects was associated with BLE beacon sensor recordings of motion (mean 407 instances; SD 365) and increased temperature (mean 0.20 °C per minute; SD 0.16 °C) (mean total magnitude 5.4 °C; SD 2.6 °C). Diary-logged duration of magnifier use (mean 42 min; SD 24) was significantly correlated with instances of motion (p < 0.001) and rate of temperature increase (p < 0.001) recorded by the BLE beacon sensors. The BLE beacon sensors reliably detected meaningfully increased temperature, coupled with numerous instances of motion, when magnifiers were used for typical reading tasks at home by people with LV. Full article

(This article belongs to the Special Issue Sensors in Vision Research and Ophthalmic Instrumentation)

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