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Applied Sciences
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Biomedical Engineering Applications in Vision Science
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Biomedical Engineering Applications in Vision Science

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 August 2021) | Viewed by 7811

Special Issue Editor

Prof. Dr. R. Theodore Smith

E-Mail Website
Guest Editor
Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
Interests: retinal imaging; hyperspectral imaging; image analysis; artificial intelligence; telemedicine; adaptive optics; age-related macular degeneration; ophthalmic genetics; retinal degenerations; stem cell therapy

Special Issue Information

Dear Colleagues,

The diagnosis and treatment of eye disease is now inseparable from biomedical engineering in all its applications. The focus of this Special Issue of Applied Sciences is to highlight a wide range of such applications in vision science, as they  are poised to transform the field. We invite you to submit your research in the form of an original research paper, a mini-review, or a perspective article.

An explosion of new imaging devices and image analysis techniques are leading the way in non-invasive research and diagnosis. The clear ocular media offer opportunities for direct observation of structures and processes that are hidden in other systems. Rapid progress is being made in resolution and speed of acquisition of structural OCT and vascular OCTA, while other retinal imaging techniques such as autofluorescence (AF) imaging, fluorescence lifetime imaging (FLIO), and hyperspectral imaging can be precisely tuned to individual molecules of physiologic importance: the fluorophores of AMD, oximetry of retinal vessels, and even perhaps amyloid deposits signifying early AD. Hyperspectral autofluorescence imaging is bench science poised not only to unravel the molecular basis of RPE fluorescence, but also to be translated into a clinical camera for earliest detection of AMD.

Eye surgery itself is intensely demanding in precision and intolerant of error. Robotic solutions for remote surgery or performance of spatially precise tasks are transforming this domain. Operations now routine could not have been performed at all without extraordinary engineering advances in surgical instrumentation, and there are more to come.

Tissue engineering for tissue replacement or to enable transplantation is showing great promise, as are nanoparticles for drug and gene delivery. Telemedicine, with remote retinal imaging for DR, AMD, and glaucoma, coupled with deep learning artificial intelligence will transform the very fabric of future medical care.

The field is wide open as never before.

Professor R. Theodore Smith
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Autofluorescence imaging
  • OCT and OCTA imaging
  • Surgical instrumentation
  • Robotics
  • Artificial intelligence
  • Deep learning
  • Telemedicine
  • Hyperspectral imaging
  • Photoacoustic imaging
  • Age-related macular degeneration
  • Fluorescence lifetime imaging
  • Adaptive optics

Published Papers (3 papers)

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Research

10 pages, 2072 KiB  
Article
Validated Filter-Based Photoreceptor Count Algorithm on Retinal Heidelberg High Magnification Module™ Images in Healthy and Pathological Conditions
by Timo Mulders, Patty Dhooge, Ludo van der Zanden, Carel B. Hoyng and Thomas Theelen
Appl. Sci. 2021, 11(12), 5347; https://doi.org/10.3390/app11125347 - 09 Jun 2021
Cited by 3 | Viewed by 1943
Abstract
Recently introduced, the Heidelberg Engineering™ high magnification module enables in vivo visualization of cone photoreceptor cells. Currently, a reliable analysis of cone mosaic on high magnification module images is hindered by an unfavorable signal-to-noise ratio. In this paper, we describe how a novel [...] Read more.
Recently introduced, the Heidelberg Engineering™ high magnification module enables in vivo visualization of cone photoreceptor cells. Currently, a reliable analysis of cone mosaic on high magnification module images is hindered by an unfavorable signal-to-noise ratio. In this paper, we describe how a novel high magnification module high-pass filter may enhance cone signals in healthy participants and patients. We compared the cone counts of our filter-based algorithm to the counts of two human graders. We found a good to excellent intragrader and intergrader correlation in both patients and healthy participants. We identified a good correlation between the average cone counts of both graders and high-pass filter cone counts in patients and healthy participants. We observed no significant difference between manual and filter-based counts via the Bland–Altman analysis. In conclusion, a quantitative cone analysis on high magnification module images is feasible manually by human graders and automatically by a filter-based algorithm. However, larger datasets are needed to improve repeatability and consistency by training human graders. Full article
(This article belongs to the Special Issue Biomedical Engineering Applications in Vision Science)
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10 pages, 9962 KiB  
Article
Leveraging the Generalization Ability of Deep Convolutional Neural Networks for Improving Classifiers for Color Fundus Photographs
by Jaemin Son, Jaeyoung Kim, Seo Taek Kong and Kyu-Hwan Jung
Appl. Sci. 2021, 11(2), 591; https://doi.org/10.3390/app11020591 - 09 Jan 2021
Cited by 3 | Viewed by 2754
Abstract
Deep learning demands a large amount of annotated data, and the annotation task is often crowdsourced for economic efficiency. When the annotation task is delegated to non-experts, the dataset may contain data with inaccurate labels. Noisy labels not only yield classification models with [...] Read more.
Deep learning demands a large amount of annotated data, and the annotation task is often crowdsourced for economic efficiency. When the annotation task is delegated to non-experts, the dataset may contain data with inaccurate labels. Noisy labels not only yield classification models with sub-optimal performance, but may also impede their optimization dynamics. In this work, we propose exploiting the pattern recognition capacity of deep convolutional neural networks to filter out supposedly mislabeled cases while training. We suggest a training method that references softmax outputs to judge the correctness of the given labels. This approach achieved outstanding performance compared to the existing methods in various noise settings on a large-scale dataset (Kaggle 2015 Diabetic Retinopathy). Furthermore, we demonstrate a method mining positive cases from a pool of unlabeled images by exploiting the generalization ability. With this method, we won first place on the offsite validation dataset in pathological myopia classification challenge (PALM), achieving the AUROC of 0.9993 in the final submission. Source codes are publicly available. Full article
(This article belongs to the Special Issue Biomedical Engineering Applications in Vision Science)
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18 pages, 1966 KiB  
Article
A New Gaze Estimation Method Based on Homography Transformation Derived from Geometric Relationship
by Kaiqing Luo, Xuan Jia, Hua Xiao, Dongmei Liu, Li Peng, Jian Qiu and Peng Han
Appl. Sci. 2020, 10(24), 9079; https://doi.org/10.3390/app10249079 - 18 Dec 2020
Cited by 3 | Viewed by 2373
Abstract
In recent years, the gaze estimation system, as a new type of human-computer interaction technology, has received extensive attention. The gaze estimation model is one of the main research contents of the system. The quality of the model will directly affect the accuracy [...] Read more.
In recent years, the gaze estimation system, as a new type of human-computer interaction technology, has received extensive attention. The gaze estimation model is one of the main research contents of the system. The quality of the model will directly affect the accuracy of the entire gaze estimation system. To achieve higher accuracy even with simple devices, this paper proposes an improved mapping equation model based on homography transformation. In the process of experiment, the model mainly uses the “Zhang Zhengyou calibration method” to obtain the internal and external parameters of the camera to correct the distortion of the camera, and uses the LM(Levenberg-Marquardt) algorithm to solve the unknown parameters contained in the mapping equation. After all the parameters of the equation are determined, the gaze point is calculated. Different comparative experiments are designed to verify the experimental accuracy and fitting effect of this mapping equation. The results show that the method can achieve high experimental accuracy, and the basic accuracy is kept within 0.6∘. The overall trend shows that the mapping method based on homography transformation has higher experimental accuracy, better fitting effect and stronger stability. Full article
(This article belongs to the Special Issue Biomedical Engineering Applications in Vision Science)
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