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Applied Sciences
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New Insight in Biomedicine: Optics, Ultrasound and Imaging
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New Insight in Biomedicine: Optics, Ultrasound and Imaging

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 (31 August 2023) | Viewed by 4476

Special Issue Editors

Dr. Syed Islam

E-Mail Website
Guest Editor
Centre for Artificial Intelligence and Machine Learning, Discipline of Computing and Security, School of Science, Edith Cowan University, Joondalup, Australia
Interests: artificial intelligence; pattern recognition; 2D and 3D image processing; computer graphics; ear and face biometrics; medical imaging; database systems; telerobotics
Special Issues, Collections and Topics in MDPI journals
Dr. Yasmeen Mourice George

E-Mail Website
Guest Editor
Department of Data Science & AI, Monash University, Bandar Sunway, Malaysia
Interests: artificial intelligence; data science; health care data analytics across domains of radiology; dermatology; ophthalmology; biomedical informatics; computer vision
Dr. Sajib Saha

E-Mail Website
Guest Editor
Commonwealth Scientific and Industrial Research Organization, Perth, Australia
Interests: biomedical imaging and image processing; tele-health; computer vision; pattern recognition and data mining; expert systems; signal processing; artificial intelligence and its application in ophthalmology; oncology; osteology and podiatry
Dr. Syed Afaq Ali Shah

E-Mail Website
Guest Editor
Centre for Artificial Intelligence and Machine Learning, Discipline of Computing and Security, School of Science, Edith Cowan University, Joondalup, Australia
Interests: artificial intelligence; machine learning; deep learning; pattern recognition; computer vision; health analytics; facial analysis; robotics and autonomous systems; object detection; recognition and segmentation; image/video processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the past decade, there has been an explosion in the development of novel optics, ultrasound and imaging techniques in biomedical research and clinical applications. Significant progresses have been observed in to provide new and improved biological information contributing to better diagnosis and in-depth understanding of the biological processes. Moreover, recent advances in machine learning or in particular deep learning has significantly advanced the diagnostic capability and have enabled novel diagnostic and treatment approaches for different types of diseases.

This special issue aims to report the latest advances in biomedical optics, ultrasound and other imaging techniques along with the state-of-the-art machine learning algorithms for analyzing them for early prediction, diagnosis or treatment planning for various diseases. 

This special issue will publish high-quality, original research and review papers, in the following fields: 

  • Explainable AI for medical imaging
  • Imbalance data problem in medical image analysis
  • Optical coherence tomography, color fundus photography, X-rays, CT, ultrasound, whole slide and other medical image modalities
  • Registration and segmentation of medical images
  • Disease prediction, diagnosis and prognosis using medical images
  • Medical image enhancement and noise reduction
  • Machine learning and deep learning
  • Medical image processing and reconstruction
  • Optical image guided surgery
  • Multimodal imaging or sensor integration

Dr. Syed Islam
Dr. Yasmeen Mourice George
Dr. Sajib Saha
Dr. Syed Afaq Ali Shah
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. 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

  • biomedical optics
  • diagnostic imaging
  • machine learning
  • deep learning
  • AI for medical imaging
  • treatment planning

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

16 pages, 2359 KiB  
Article
Using Ensemble OCT-Derived Features beyond Intensity Features for Enhanced Stargardt Atrophy Prediction with Deep Learning
by Zubin Mishra, Ziyuan Wang, SriniVas R. Sadda and Zhihong Hu
Appl. Sci. 2023, 13(14), 8555; https://doi.org/10.3390/app13148555 - 24 Jul 2023
Cited by 2 | Viewed by 1354
Abstract
Stargardt disease is the most common form of juvenile-onset macular dystrophy. Spectral-domain optical coherence tomography (SD-OCT) imaging provides an opportunity to directly measure changes to retinal layers due to Stargardt atrophy. Generally, atrophy segmentation and prediction can be conducted using mean intensity feature [...] Read more.
Stargardt disease is the most common form of juvenile-onset macular dystrophy. Spectral-domain optical coherence tomography (SD-OCT) imaging provides an opportunity to directly measure changes to retinal layers due to Stargardt atrophy. Generally, atrophy segmentation and prediction can be conducted using mean intensity feature maps generated from the relevant retinal layers. In this paper, we report an approach using advanced OCT-derived features to augment and enhance data beyond the commonly used mean intensity features for enhanced prediction of Stargardt atrophy with an ensemble deep learning neural network. With all the relevant retinal layers, this neural network architecture achieves a median Dice coefficient of 0.830 for six-month predictions and 0.828 for twelve-month predictions, showing a significant improvement over a neural network using only mean intensity, which achieved Dice coefficients of 0.744 and 0.762 for six-month and twelve-month predictions, respectively. When using feature maps generated from different layers of the retina, significant differences in performance were observed. This study shows promising results for using multiple OCT-derived features beyond intensity for assessing the prognosis of Stargardt disease and quantifying the rate of progression. Full article
(This article belongs to the Special Issue New Insight in Biomedicine: Optics, Ultrasound and Imaging)
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18 pages, 2337 KiB  
Article
Wave-Shaped Microstructure Cancer Detection Sensor in Terahertz Band: Design and Analysis
by Md Rezaul Hoque Khan, Atiqul Alam Chowdhury, Mohammad Rakibul Islam, Md Sanowar Hosen, Mhamud Hasan Mim and Mirza Muntasir Nishat
Appl. Sci. 2023, 13(9), 5784; https://doi.org/10.3390/app13095784 - 8 May 2023
Cited by 5 | Viewed by 2225
Abstract
For the quick identification of diverse types of cancer/malignant cells in the human body, a new hollow-core optical waveguide based on Photonic Crystal Fiber (PCF) is proposed and numerically studied. The refractive index (RI) differs between normal and cancerous cells, and it is [...] Read more.
For the quick identification of diverse types of cancer/malignant cells in the human body, a new hollow-core optical waveguide based on Photonic Crystal Fiber (PCF) is proposed and numerically studied. The refractive index (RI) differs between normal and cancerous cells, and it is through this distinction that the other crucial optical parameters are assessed. The proposed cancer cell biosensor’s guiding characteristics are examined in the COMSOL Multiphysics v5.5 environment. The Finite Element Method (FEM) framework is used to quantify the display of the suggested fiber biosensor. Extremely fine mesh elements are additionally added to guarantee the highest simulation accuracy. The simulation results on the suggested sensor model achieve a very high relative sensitivity of 99.9277%, 99.9243%, 99.9302%, 99.9314%, 99.9257% and 99.9169%, a low effective material loss of 8.55×10−5 cm−1, 8.96×10−5 cm−1, 8.24×10−5 cm−1, 8.09×10−5 cm−1, 8.79×10−5 cm−1, and 9.88×10−5 cm−1 for adrenal gland cancer, blood cancer, breast cancer type-1, breast cancer type-2, cervical cancer, and skin cancer, respectively, at a 3.0 THz frequency regime. A very low confinement loss of 6.1×10−10 dB/cm is also indicated by the simulation findings for all of the cancer cases that were mentioned. The straightforward PCF structure of the proposed biosensor offers a high likelihood of implementation when used in conjunction with these conventional performance indexes. So, it appears that this biosensor will create new opportunities for the identification and diagnosis of various cancer cells. Full article
(This article belongs to the Special Issue New Insight in Biomedicine: Optics, Ultrasound and Imaging)
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