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Optics
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New Discoveries in Biomedical Optical Imaging and Sensing: From Technologies...
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New Discoveries in Biomedical Optical Imaging and Sensing: From Technologies to Applications

A special issue of Optics (ISSN 2673-3269). This special issue belongs to the section "Biomedical Optics".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 6384

Special Issue Editor

Prof. Dr. Yung-Kang Shen

E-Mail Website
Guest Editor
School of Dental Technology, College of Oral College, Taipei Medical University, Taipei 110, Taiwan
Interests: 3D scaffold; biomaterials micro/nano fabrication; 3D/4D printing & bioprinting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical optical imaging and sensing are rapidly growing in recent years and they are the most relied-upon tools in health care for diagnosis and treatment of human diseases. Novel developments in both hardware and software systems have enabled new ways of detecting, monitoring and tracking health-related biomarkers in clinical, pre-clinical, and home environments. The biomedical optical imaging technology is also a very important part of the optics science applications.

The field of biomedical optical imaging consists of a myriad of techniques, devices, instruments, probes, computer algorithms and software, animal studies, and clinical trials. With the developments in computational power and sensors, new sensing technologies and new analysis methods appears. Our special issue aims to bring a new platform for researchers to report the novel methods and results in biomedical optical imaging and sensing technologies. The scopes and topics include, but not limited to:

  • Image analysis for disease detection and/or monitoring, such as  MRI, X-ray, PET, ultrasound, etc.
  • Biomedical optical imaging and sensing technologies, such as diffuse optical spectroscopy, near infrared spectroscopy, diffuse correlation spectroscopy, spatial frequency domain imaging, diffuse optical imaging, optical coherence tomography, photoacoustic imaging, microscopy, optical fibers and sensors, etc.
  • Multimodal systems for diagnosis, treatment, and/or prevention.
  • Clinical applications of novel biomedical sensing technologies, such as cardiovascular and cardiopulmonary, neurovascular, critical care, sepsis, home monitoring, aging, etc.

Prof. Dr. Yung-Kang Shen
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. Optics is an international peer-reviewed open access quarterly 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 1200 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

  • biomedical imaging and sensing technologies
  • image processing
  • diffuse optical spectroscopy
  • photoacoustic imaging

Published Papers (2 papers)

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Research

11 pages, 3891 KiB  
Article
Automated Clinical Decision Support for Coronary Plaques Characterization from Optical Coherence Tomography Imaging with Fused Neural Networks
by Haroon Zafar, Junaid Zafar and Faisal Sharif
Optics 2022, 3(1), 8-18; https://doi.org/10.3390/opt3010002 - 10 Jan 2022
Cited by 2 | Viewed by 2365
Abstract
Deep Neural Networks (DNNs) are nurturing clinical decision support systems for the detection and accurate modeling of coronary arterial plaques. However, efficient plaque characterization in time-constrained settings is still an open problem. The purpose of this study is to develop a novel automated [...] Read more.
Deep Neural Networks (DNNs) are nurturing clinical decision support systems for the detection and accurate modeling of coronary arterial plaques. However, efficient plaque characterization in time-constrained settings is still an open problem. The purpose of this study is to develop a novel automated classification architecture viable for the real-time clinical detection and classification of coronary artery plaques, and secondly, to use the novel dataset of OCT images for data augmentation. Further, the purpose is to validate the efficacy of transfer learning for arterial plaques classification. In this perspective, a novel time-efficient classification architecture based on DNNs is proposed. A new data set consisting of in-vivo patient Optical Coherence Tomography (OCT) images labeled by three trained experts was created and dynamically programmed. Generative Adversarial Networks (GANs) were used for populating the coronary aerial plaques dataset. We removed the fully connected layers, including softmax and the cross-entropy in the GoogleNet framework, and replaced them with the Support Vector Machines (SVMs). Our proposed architecture limits weight up-gradation cycles to only modified layers and computes the global hyper-plane in a timely, competitive fashion. Transfer learning was used for high-level discriminative feature learning. Cross-entropy loss was minimized by using the Adam optimizer for model training. A train validation scheme was used to determine the classification accuracy. Automated plaques differentiation in addition to their detection was found to agree with the clinical findings. Our customized fused classification scheme outperforms the other leading reported works with an overall accuracy of 96.84%, and multiple folds reduced elapsed time demonstrating it as a viable choice for real-time clinical settings. Full article
(This article belongs to the Special Issue New Discoveries in Biomedical Optical Imaging and Sensing: From Technologies to Applications)
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14 pages, 3041 KiB  
Article
Biochemical Profiles of In Vivo Oral Mucosa by Using a Portable Raman Spectroscopy System
by Marcelo Saito Nogueira, Victoria Ribeiro, Marianna Pires, Felipe Peralta and Luis Felipe das Chagas e Silva de Carvalho
Optics 2021, 2(3), 134-147; https://doi.org/10.3390/opt2030013 - 16 Jul 2021
Cited by 10 | Viewed by 3196
Abstract
Most oral injuries are diagnosed by histopathological analysis of invasive and time-consuming biopsies. This analysis and conventional clinical observation cannot identify biochemically altered tissues predisposed to malignancy if no microstructural changes are detectable. With this in mind, detailed biochemical characterization of normal tissues [...] Read more.
Most oral injuries are diagnosed by histopathological analysis of invasive and time-consuming biopsies. This analysis and conventional clinical observation cannot identify biochemically altered tissues predisposed to malignancy if no microstructural changes are detectable. With this in mind, detailed biochemical characterization of normal tissues and their differentiation features on healthy individuals is important in order to recognize biomolecular changes associated with early tissue predisposition to malignant transformation. Raman spectroscopy is a label-free method for characterization of tissue structure and specific composition. In this study, we used Raman spectroscopy to characterize the biochemistry of in vivo oral tissues of healthy individuals. We investigated this biochemistry based on the vibrational modes related to Raman spectra of four oral subsites (buccal, gingiva, lip and tongue) of ten volunteers as well as with principal component (PC) loadings for the difference between the four types of oral subsites. Therefore, we determined the biochemical characteristics of each type of healthy oral subsite and those corresponding to differentiation of the four types of subsites. In addition, we developed a spectral reference of oral healthy tissues of individuals in the Brazilian population for future diagnosis of early pathological conditions using real-time, noninvasive and label-free techniques such as Raman spectroscopy. Full article
(This article belongs to the Special Issue New Discoveries in Biomedical Optical Imaging and Sensing: From Technologies to Applications)
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