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Bioengineering
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Biomedical Applications of Optical Coherence Tomography
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Biomedical Applications of Optical Coherence Tomography

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 15731

Special Issue Editors

Dr. Antonia Lichtenegger

E-Mail Website
Guest Editor
Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria
Interests: tomography; optical coherence; optics; photonics
Dr. Danielle J. Harper

E-Mail Website
Guest Editor
Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA
Interests: retinal imaging; optical coherence tomography; medical physics; optics; biophotonics

Special Issue Information

Dear Colleagues,

Since its invention in 1991, optical coherence tomography (OCT) has rapidly become one of the most important clinical tools in the field of ophthalmology. In recent years, applications of this inherently non-invasive and label-free three-dimensional imaging technique have extended far beyond retinal and corneal imaging. OCT applications are being increasingly used in medicine in fields including dermatology, endoscopy and neurology, and advances in system technology and image processing mean that the resultant images are approaching cellular resolution over a wide field of view. Through the constant development of new technologies and functional extensions, OCT continues to move into new areas of biomedical application.

This Special Issue will focus on the most recent advances in biomedical applications of optical coherence tomography. The addressed topics include, but are not limited to:

  • Optical coherence tomography;
  • Optical coherence microscopy;
  • Optical coherence angiography;
  • Dynamic OCT;
  • Doppler OCT;
  • Polarization-sensitive OCT;
  • Spectroscopic OCT;
  • Optical coherence elastography;
  • OCT and artificial intelligence;
  • OCT for basic research (ex vivo, in vitro or in vivo models);
  • OCT for clinical studies in the field of biomedical research.

Original research contributions will be prioritized, but critical reviews about the state of the art, current limitations and future perspectives are also welcome.

Dr. Antonia Lichtenegger
Dr. Danielle J. Harper
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 2700 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

  • retinal imaging
  • optical coherence tomography
  • optics
  • biophotonics

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Published Papers (5 papers)

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Research

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19 pages, 1054 KiB  
Article
RVM-GSM: Classification of OCT Images of Genitourinary Syndrome of Menopause Based on Integrated Model of Local–Global Information Pattern
by Kaiwen Song, Haoran Wang, Xinyu Guo, Mingyang Sun, Yanbin Shao, Songfeng Xue, Hongwei Zhang and Tianyu Zhang
Bioengineering 2023, 10(4), 450; https://doi.org/10.3390/bioengineering10040450 - 6 Apr 2023
Viewed by 2084
Abstract
Genitourinary syndrome of menopause (GSM) is a group of syndromes, including atrophy of the reproductive tract and urinary tract, and sexual dysfunction, caused by decreased levels of hormones, such as estrogen, in women during the transition to, or late stage of, menopause. GSM [...] Read more.
Genitourinary syndrome of menopause (GSM) is a group of syndromes, including atrophy of the reproductive tract and urinary tract, and sexual dysfunction, caused by decreased levels of hormones, such as estrogen, in women during the transition to, or late stage of, menopause. GSM symptoms can gradually become severe with age and menopausal time, seriously affecting the safety, and physical and mental health, of patients. Optical coherence tomography (OCT) systems can obtain images similar to “optical slices” in a non-destructive manner. This paper presents a neural network, called RVM-GSM, to implement automatic classification tasks for different types of GSM-OCT images. The RVM-GSM module uses a convolutional neural network (CNN) and a vision transformer (ViT) to capture local and global features of the GSM-OCT images, respectively, and, then, fuses the two features in a multi-layer perception module to classify the image. In accordance with the practical needs of clinical work, lightweight post-processing is added to the final surface of the RVM-GSM module to compress the module. Experimental results showed that the accuracy rate of RVM-GSM in the GSM-OCT image classification task was 98.2%. This result is better than those of the CNN and Vit models, demonstrating the promise and potential of the application of RVM-GSM in the physical health and hygiene fields for women. Full article
(This article belongs to the Special Issue Biomedical Applications of Optical Coherence Tomography)
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12 pages, 1654 KiB  
Article
Swept-Source OCT Mid-Peripheral Retinal Irregularity in Retinal Detachment and Posterior Vitreous Detachment Eyes
by Stewart R. Lake, Murk J. Bottema, Tyra Lange, Keryn A. Williams and Karen J. Reynolds
Bioengineering 2023, 10(3), 377; https://doi.org/10.3390/bioengineering10030377 - 19 Mar 2023
Cited by 1 | Viewed by 2424
Abstract
Irregularities in retinal shape have been shown to correlate with axial length, a major risk factor for retinal detachment. To further investigate this association, a comparison was performed of the swept-source optical coherence tomography (SS OCT) peripheral retinal shape of eyes that had [...] Read more.
Irregularities in retinal shape have been shown to correlate with axial length, a major risk factor for retinal detachment. To further investigate this association, a comparison was performed of the swept-source optical coherence tomography (SS OCT) peripheral retinal shape of eyes that had either a posterior vitreous detachment (PVD) or vitrectomy for retinal detachment. The objective was to identify a biomarker that can be tested as a predictor for retinal detachment. Eyes with a PVD (N = 88), treated retinal detachment (N = 67), or retinal tear (N = 53) were recruited between July 2020 and January 2022 from hospital retinal clinics in South Australia. The mid-peripheral retina was imaged in four quadrants with SS OCT. The features explored were patient age, eye axial length, and retinal shape irregularity quantified in the frequency domain. A discriminant analysis classifier to identify retinal detachment eyes was trained with two-thirds and tested with one-third of the sample. Retinal detachment eyes had greater irregularity than PVD eyes. A classifier trained using shape features from the superior and temporal retina had a specificity of 84% and a sensitivity of 48%. Models incorporating axial length were less successful, suggesting peripheral retinal irregularity is a better biomarker for retinal detachment than axial length. Mid-peripheral retinal irregularity can identify eyes that have experienced a retinal detachment. Full article
(This article belongs to the Special Issue Biomedical Applications of Optical Coherence Tomography)
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15 pages, 1173 KiB  
Article
CTS-Net: A Segmentation Network for Glaucoma Optical Coherence Tomography Retinal Layer Images
by Songfeng Xue, Haoran Wang, Xinyu Guo, Mingyang Sun, Kaiwen Song, Yanbin Shao, Hongwei Zhang and Tianyu Zhang
Bioengineering 2023, 10(2), 230; https://doi.org/10.3390/bioengineering10020230 - 8 Feb 2023
Cited by 8 | Viewed by 2581
Abstract
Optical Coherence Tomography (OCT) technology is essential to obtain glaucoma diagnostic data non-invasively and rapidly. Early diagnosis of glaucoma can be achieved by analyzing the thickness and shape of retinal layers. Accurate retinal layer segmentation assists ophthalmologists in improving the efficiency of disease [...] Read more.
Optical Coherence Tomography (OCT) technology is essential to obtain glaucoma diagnostic data non-invasively and rapidly. Early diagnosis of glaucoma can be achieved by analyzing the thickness and shape of retinal layers. Accurate retinal layer segmentation assists ophthalmologists in improving the efficiency of disease diagnosis. Deep learning technology is one of the most effective methods for processing OCT retinal layer images, which can segment different retinal layers and effectively obtain the topological structure of the boundary. This paper proposes a neural network method for retinal layer segmentation based on the CSWin Transformer (CTS-Net), which can achieve pixel-level segmentation and obtain smooth boundaries. A Dice loss function based on boundary areas (BADice Loss) is proposed to make CTS-Net learn more features of edge regions and improve the accuracy of boundary segmentation. We applied the model to the publicly available dataset of glaucoma retina, and the test results showed that mean absolute distance (MAD), root mean square error (RMSE), and dice-similarity coefficient (DSC) metrics were 1.79 pixels, 2.15 pixels, and 92.79%, respectively, which are better than those of the compared model. In the cross-validation experiment, the ranges of MAD, RMSE, and DSC are 0.05 pixels, 0.03 pixels, and 0.33%, respectively, with a slight difference, which further verifies the generalization ability of CTS-Net. Full article
(This article belongs to the Special Issue Biomedical Applications of Optical Coherence Tomography)
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16 pages, 3026 KiB  
Article
Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning
by Juhwan Lee, Justin N. Kim, Lia Gomez-Perez, Yazan Gharaibeh, Issam Motairek, Gabriel T. R. Pereira, Vladislav N. Zimin, Luis A. P. Dallan, Ammar Hoori, Sadeer Al-Kindi, Giulio Guagliumi, Hiram G. Bezerra and David L. Wilson
Bioengineering 2022, 9(11), 648; https://doi.org/10.3390/bioengineering9110648 - 3 Nov 2022
Cited by 13 | Viewed by 2791
Abstract
Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total [...] Read more.
Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,θ) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71 ± 0.10 and pixel-wise sensitivity/specificity of 87.7 ± 6.6%/99.8 ± 0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5 ± 0.3%, specificity of 98.8 ± 1.0%, and accuracy of 99.1 ± 0.5%. The classification step eliminated the majority of residual false positives and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared with 730 from manual analysis, representing a 4.4% difference. When compared with the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning. Full article
(This article belongs to the Special Issue Biomedical Applications of Optical Coherence Tomography)
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Review

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18 pages, 3103 KiB  
Review
Optical Coherence Tomography Is a Promising Tool for Zebrafish-Based Research—A Review
by Antonia Lichtenegger, Bernhard Baumann and Yoshiaki Yasuno
Bioengineering 2023, 10(1), 5; https://doi.org/10.3390/bioengineering10010005 - 20 Dec 2022
Cited by 15 | Viewed by 4121
Abstract
The zebrafish is an established vertebrae model in the field of biomedical research. With its small size, rapid maturation time and semi-transparency at early development stages, it has proven to be an important animal model, especially for high-throughput studies. Three-dimensional, high-resolution, non-destructive and [...] Read more.
The zebrafish is an established vertebrae model in the field of biomedical research. With its small size, rapid maturation time and semi-transparency at early development stages, it has proven to be an important animal model, especially for high-throughput studies. Three-dimensional, high-resolution, non-destructive and label-free imaging techniques are perfectly suited to investigate these animals over various development stages. Optical coherence tomography (OCT) is an interferometric-based optical imaging technique that has revolutionized the diagnostic possibilities in the field of ophthalmology and has proven to be a powerful tool for many microscopic applications. Recently, OCT found its way into state-of-the-art zebrafish-based research. This review article gives an overview and a discussion of the relevant literature and an outlook for this emerging field. Full article
(This article belongs to the Special Issue Biomedical Applications of Optical Coherence Tomography)
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