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Special Issue "Biomedical Imaging and Analysis of the Eye"

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A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 15 November 2023 | Viewed by 4019

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Special Issue Editors

Dr. Xuejun Qian

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Guest Editor

School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
Interests: biomedical imaging; multi-modal imaging; functional imaging; artificial intelligence

Prof. Dr. Qifa Zhou

E-Mail Website
Guest Editor

Department of Biomedical Engineering and Ophthalmology, University of Southern California, Los Angeles, CA 90007, USA
Interests: MEMS; biomedical imaging; photoacoustic imaging; ultrasound; elastography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The eye is an important aspect of human health since people rely on their eyes to see and make sense of the world around them. Investigating the condition of the eye is not only important to identify the eye diseases itself but also to predict the risk of other non-ocular diseases such as diabetes, anemia, cardiovascular risk, chronic kidney disease, and other systemic parameters.

The scope of this Special Issue is to provide a forum across medical imaging modalities and machine/deep learning tools to advance the development of algorithms, systems, and clinical applicability for imaging and analyzing the eye. We welcome both original research and review articles. Potential topics include but are not limited to the following:

Advanced imaging technique and system for the eye;
Machine- or deep-learning-based image analysis using eye imaging;
Imaging-guided eye surgery and treatment;
Image processing in ocular imaging;
Mathematical or statistical modeling of the eye;
Multi-dimensional and multi-modality fusion ocular imaging.

Dr. Xuejun Qian
Prof. Dr. Qifa Zhou
Guest Editors

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. Bioengineering is an international peer-reviewed open access monthly 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 2000 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

ultrasound imaging
optical coherence tomography
functional imaging
machine/deep learning
ocular tissue

Published Papers (5 papers)

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Research

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Open AccessArticle

Non-Invasive Hybrid Ultrasound Stimulation of Visual Cortex In Vivo

Deepthi S. Rajendran Nair

and

Bioengineering 2023, 10(5), 577; https://doi.org/10.3390/bioengineering10050577 - 10 May 2023

Viewed by 549

Abstract

The optic nerve is the second cranial nerve (CN II) that connects and transmits visual information between the retina and the brain. Severe damage to the optic nerve often leads to distorted vision, vision loss, and even blindness. Such damage can be caused by various types of degenerative diseases, such as glaucoma and traumatic optic neuropathy, and result in an impaired visual pathway. To date, researchers have not found a viable therapeutic method to restore the impaired visual pathway; however, in this paper, a newly synthesized model is proposed to bypass the damaged portion of the visual pathway and set up a direct connection between a stimulated visual input and the visual cortex (VC) using Low-frequency Ring-transducer Ultrasound Stimulation (LRUS). In this study, by utilizing and integrating various advanced ultrasonic and neurological technologies, the following advantages are achieved by the proposed LRUS model: 1. This is a non-invasive procedure that uses enhanced sound field intensity to overcome the loss of ultrasound signal due to the blockage of the skull. 2. The simulated visual signal generated by LRUS in the visual-cortex-elicited neuronal response in the visual cortex is comparable to light stimulation of the retina. The result was confirmed by a combination of real-time electrophysiology and fiber photometry. 3. VC showed a faster response rate under LRUS than light stimulation through the retina. These results suggest a potential non-invasive therapeutic method for restoring vision in optic-nerve-impaired patients using ultrasound stimulation (US). Full article

(This article belongs to the Special Issue Biomedical Imaging and Analysis of the Eye)

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Open AccessArticle

Reliability of Retinal Layer Annotation with a Novel, High-Resolution Optical Coherence Tomography Device: A Comparative Study

Leon von der Emde

Marlene Saßmannshausen

Olivier Morelle

Geena Rennen

Frank G. Holz

Maximilian W. M. Wintergerst

and

Thomas Ach

Bioengineering 2023, 10(4), 438; https://doi.org/10.3390/bioengineering10040438 - 31 Mar 2023

Viewed by 932

Abstract

Optical coherence tomography (OCT) enables in vivo diagnostics of individual retinal layers in the living human eye. However, improved imaging resolution could aid diagnosis and monitoring of retinal diseases and identify potential new imaging biomarkers. The investigational high-resolution OCT platform (High-Res OCT; 853 nm central wavelength, 3 µm axial-resolution) has an improved axial resolution by shifting the central wavelength and increasing the light source bandwidth compared to a conventional OCT device (880 nm central wavelength, 7 µm axial-resolution). To assess the possible benefit of a higher resolution, we compared the retest reliability of retinal layer annotation from conventional and High-Res OCT, evaluated the use of High-Res OCT in patients with age-related macular degeneration (AMD), and assessed differences of both devices on subjective image quality. Thirty eyes of 30 patients with early/intermediate AMD (iAMD; mean age 75 ± 8 years) and 30 eyes of 30 age-similar subjects without macular changes (62 ± 17 years) underwent identical OCT imaging on both devices. Inter- and intra-reader reliability were analyzed for manual retinal layer annotation using EyeLab. Central OCT B-scans were graded for image quality by two graders and a mean-opinion-score (MOS) was formed and evaluated. Inter- and intra-reader reliability were higher for High-Res OCT (greatest benefit for inter-reader reliability: ganglion cell layer; for intra-reader reliability: retinal nerve fiber layer). High-Res OCT was significantly associated with an improved MOS (MOS 9/8, Z-value = 5.4, p < 0.01) mainly due to improved subjective resolution (9/7, Z-Value 6.2, p < 0.01). The retinal pigment epithelium drusen complex showed a trend towards improved retest reliability in High-Res OCT in iAMD eyes but without statistical significance. Improved axial resolution of the High-Res OCT benefits retest reliability of retinal layer annotation and improves perceived image quality and resolution. Automated image analysis algorithms could also benefit from the increased image resolution. Full article

(This article belongs to the Special Issue Biomedical Imaging and Analysis of the Eye)

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Open AccessArticle

Detecting Early Ocular Choroidal Melanoma Using Ultrasound Localization Microscopy

and

Bioengineering 2023, 10(4), 428; https://doi.org/10.3390/bioengineering10040428 - 28 Mar 2023

Viewed by 658

Abstract

Ocular choroidal melanoma (OCM) is the most common ocular primary malignant tumor in adults, and there is an increasing emphasis on its early detection and treatment worldwide. The main obstacle in early detection of OCM is its overlapping clinical features with benign choroidal nevus. Thus, we propose ultrasound localization microscopy (ULM) based on the image deconvolution algorithm to assist the diagnosis of small OCM in early stages. Furthermore, we develop ultrasound (US) plane wave imaging based on three-frame difference algorithm to guide the placement of the probe on the field of view. A high-frequency Verasonics Vantage system and an L22-14v linear array transducer were used to perform experiments on both custom-made modules in vitro and a SD rat with ocular choroidal melanoma in vivo. The results demonstrate that our proposed deconvolution method implement more robust microbubble (MB) localization, reconstruction of microvasculature network in a finer grid and more precise flow velocity estimation. The excellent performance of US plane wave imaging was successfully validated on the flow phantom and in an in vivo OCM model. In the future, the super-resolution ULM, a critical complementary imaging modality, can provide doctors with conclusive suggestions for early diagnosis of OCM, which is significant for the treatment and prognosis of patients. Full article

(This article belongs to the Special Issue Biomedical Imaging and Analysis of the Eye)

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Open AccessArticle

Rotational Distortion and Compensation in Optical Coherence Tomography with Anisotropic Pixel Resolution

and

Bioengineering 2023, 10(3), 313; https://doi.org/10.3390/bioengineering10030313 - 01 Mar 2023

Viewed by 655

Abstract

Accurate image registration is essential for eye movement compensation in optical coherence tomography (OCT) and OCT angiography (OCTA). The spatial resolution of an OCT instrument is typically anisotropic, i.e., has different resolutions in the lateral and axial dimensions. When OCT images have anisotropic [...] Read more.

R D

) and false translation (

F T

) are always observed after image registration for rotational movement. In this study,

R D

and

F T

were quantitively analyzed over different degrees of rotational movement and various lateral and axial pixel resolution ratio (

R_{L} / R_{A}

) values. The

R D

and

F T

provide the evaluation criteria for image registration. The theoretical analysis confirmed that the

R D

and

F T

increase significantly with the rotation degree and

R_{L} / R_{A}

. An image resizing assisting registration (RAR) strategy was proposed for accurate image registration. The performance of direct registration (DR) and RAR for retinal OCT and OCTA images were quantitatively compared. Experimental results confirmed that unnormalized

R_{L} / R_{A}

causes

R D

and

F T

; RAR can effectively improve the performance of OCT and OCTA image registration and distortion compensation. Full article

(This article belongs to the Special Issue Biomedical Imaging and Analysis of the Eye)

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Review

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Open AccessReview

On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images

Prakash Kumar Karn

and

Waleed H. Abdulla

Bioengineering 2023, 10(4), 407; https://doi.org/10.3390/bioengineering10040407 - 24 Mar 2023

Viewed by 640

Abstract

Optical coherence tomography (OCT) is a noninvasive imaging technique that provides high-resolution cross-sectional retina images, enabling ophthalmologists to gather crucial information for diagnosing various retinal diseases. Despite its benefits, manual analysis of OCT images is time-consuming and heavily dependent on the personal experience of the analyst. This paper focuses on using machine learning to analyse OCT images in the clinical interpretation of retinal diseases. The complexity of understanding the biomarkers present in OCT images has been a challenge for many researchers, particularly those from nonclinical disciplines. This paper aims to provide an overview of the current state-of-the-art OCT image processing techniques, including image denoising and layer segmentation. It also highlights the potential of machine learning algorithms to automate the analysis of OCT images, reducing time consumption and improving diagnostic accuracy. Using machine learning in OCT image analysis can mitigate the limitations of manual analysis methods and provide a more reliable and objective approach to diagnosing retinal diseases. This paper will be of interest to ophthalmologists, researchers, and data scientists working in the field of retinal disease diagnosis and machine learning. By presenting the latest advancements in OCT image analysis using machine learning, this paper will contribute to the ongoing efforts to improve the diagnostic accuracy of retinal diseases. Full article

(This article belongs to the Special Issue Biomedical Imaging and Analysis of the Eye)

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Special Issue "Biomedical Imaging and Analysis of the Eye"

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