Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Biomedical Imaging: Present and Future Challenges, from Image Processing Sensors...
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Biomedical Imaging: Present and Future Challenges, from Image Processing Sensors through Artificial Intelligence

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 6316

Special Issue Editors

Dr. Alessandro Bruno

E-Mail Website
Guest Editor
Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
Interests: computer vision; artificial intelligence; deep learning; image analysis and processing; visual saliency; biomedical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Alessia Artesani

E-Mail Website
Guest Editor
Department of Biomedical Sciences, Humanitas University, Milan, Italy
Interests: laser-induced luminescent techniques; optical spectroscopy; microscopy
Dr. Pier Luigi Mazzeo

E-Mail Website
Guest Editor
National Reseach Council of Italy (CNR), ISASI Institute of Applied Sciences & Intelligent Systems, 80078 Pozzuoli, Italy
Interests: multimedia signal processing; image processing and understanding; image feature extraction and selection; neural network classifiers; object classification and tracking
Special Issues, Collections and Topics in MDPI journals
Dr. Faraz Janan

E-Mail Website
Guest Editor
Faculty of Engineering, Department of Bioengineering, Imperial College London, South Kensington Campus, London, UK
Interests: medical image analysis; computer vision; the application of artificial intelligence to healthcare

Special Issue Information

Dear Colleagues,

Recently, we have seen a growing interest in biomedical imaging, which enables the visualization of the structure and functions of biological objects. Biomedical imaging integrates physics, engineering, fundamental biology, and clinical medicine. Recent advances in modern sensors for the analysis of biomedical signals and images have enhanced healthcare efficacy, including in the screening and diagnosis of many diseases, novel treatment methods, self-monitoring, and disease detection. With the development and progress of biomedical imaging technology, biomedical imaging has become an essential tool in daily medical diagnostics. Therefore, biomedical image processing has become more and more important in biomedical research and clinical medicine.

This Special Issue aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of (bio)medical imaging. The topics of interest include, but are not limited to, the following:

  • Biomedical image analysis and other analyses (including, but not limited to, image quality improvement, image restoration, image segmentation, image registration, and radiomics analysis);
  • Biomedical sensing;
  • Biomedical imaging and diagnosis;
  • Image-guided therapy;
  • Computer-aided diagnosis and surgery;
  • Digital radiography;
  • X-ray computed tomography (CT);
  • Positron emission tomography (PET);
  • Ultrasound imaging;
  • Magnetic resonance imaging (MRI);
  • Microscopies;
  • Photoacoustic imaging;
  • Deep learning;
  • Federated learning.

Dr. Alessandro Bruno
Dr. Alessia Artesani
Dr. Pier Luigi Mazzeo
Dr. Faraz Janan
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. Sensors 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 2600 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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

33 pages, 8101 KiB  
Article
DIMScern: A Framework for Discerning DIMSE Services on Remote Medical Devices
by Gunhee Kim, Dohyun Kim, Jeonghun Seo, Seyoung Lee and Wonjun Song
Sensors 2024, 24(23), 7470; https://doi.org/10.3390/s24237470 - 22 Nov 2024
Viewed by 326
Abstract
In the medical domain, computer systems in digital healthcare have increased connectivity continuously and the DICOM Message Service Element (DIMSE) protocol has a critical role in exchanging biomedical imaging data among different digital healthcare systems. As the data communication technology is used to [...] Read more.
In the medical domain, computer systems in digital healthcare have increased connectivity continuously and the DICOM Message Service Element (DIMSE) protocol has a critical role in exchanging biomedical imaging data among different digital healthcare systems. As the data communication technology is used to handle sensitive information such as patient information (e.g., patient’s name, date of birth, and address) and medical images (e.g., ultrasound, X-ray, and MRI), it has emerged as a major target for security attacks. In this work, we study security concerns on the message exchange method used in the DIMSE protocol. It is important to know which DIMSE services are available on a given healthcare IT system to an adversary and we observe that the DIMSE protocol can be implemented in various ways across products, with each supporting different DIMSE services as well. We present DIMScern, a framework for discerning DIMSE services on remote medical devices. To show the effectiveness of DIMScern, we evaluate our framework on multiple DIMSE implementations, including commercial products and libraries, and identify the supported DIMSE services of them. We demonstrate that DIMScern successfully identifies medical services that are supported differently across 22 healthcare IT systems in a remote environment. Full article
(This article belongs to the Special Issue Biomedical Imaging: Present and Future Challenges, from Image Processing Sensors through Artificial Intelligence)
20 pages, 8366 KiB  
Article
Dynamic Mode Decomposition of Multiphoton and Stimulated Emission Depletion Microscopy Data for Analysis of Fluorescent Probes in Cellular Membranes
by Daniel Wüstner, Jacob Marcus Egebjerg and Line Lauritsen
Sensors 2024, 24(7), 2096; https://doi.org/10.3390/s24072096 - 25 Mar 2024
Viewed by 1082
Abstract
An analysis of the membrane organization and intracellular trafficking of lipids often relies on multiphoton (MP) and super-resolution microscopy of fluorescent lipid probes. A disadvantage of particularly intrinsically fluorescent lipid probes, such as the cholesterol and ergosterol analogue, dehydroergosterol (DHE), is their low [...] Read more.
An analysis of the membrane organization and intracellular trafficking of lipids often relies on multiphoton (MP) and super-resolution microscopy of fluorescent lipid probes. A disadvantage of particularly intrinsically fluorescent lipid probes, such as the cholesterol and ergosterol analogue, dehydroergosterol (DHE), is their low MP absorption cross-section, resulting in a low signal-to-noise ratio (SNR) in live-cell imaging. Stimulated emission depletion (STED) microscopy of membrane probes like Nile Red enables one to resolve membrane features beyond the diffraction limit but exposes the sample to a lot of excitation light and suffers from a low SNR and photobleaching. Here, dynamic mode decomposition (DMD) and its variant, higher-order DMD (HoDMD), are applied to efficiently reconstruct and denoise the MP and STED microscopy data of lipid probes, allowing for an improved visualization of the membranes in cells. HoDMD also allows us to decompose and reconstruct two-photon polarimetry images of TopFluor-cholesterol in model and cellular membranes. Finally, DMD is shown to not only reconstruct and denoise 3D-STED image stacks of Nile Red-labeled cells but also to predict unseen image frames, thereby allowing for interpolation images along the optical axis. This important feature of DMD can be used to reduce the number of image acquisitions, thereby minimizing the light exposure of biological samples without compromising image quality. Thus, DMD as a computational tool enables gentler live-cell imaging of fluorescent probes in cellular membranes by MP and STED microscopy. Full article
(This article belongs to the Special Issue Biomedical Imaging: Present and Future Challenges, from Image Processing Sensors through Artificial Intelligence)
►▼ Show Figures

Figure 1

29 pages, 4072 KiB  
Article
Using Deep Learning and B-Splines to Model Blood Vessel Lumen from 3D Images
by Andrzej Materka and Jakub Jurek
Sensors 2024, 24(3), 846; https://doi.org/10.3390/s24030846 - 28 Jan 2024
Cited by 1 | Viewed by 1823
Abstract
Accurate geometric modeling of blood vessel lumen from 3D images is crucial for vessel quantification as part of the diagnosis, treatment, and monitoring of vascular diseases. Our method, unlike other approaches which assume a circular or elliptical vessel cross-section, employs parametric B-splines combined [...] Read more.
Accurate geometric modeling of blood vessel lumen from 3D images is crucial for vessel quantification as part of the diagnosis, treatment, and monitoring of vascular diseases. Our method, unlike other approaches which assume a circular or elliptical vessel cross-section, employs parametric B-splines combined with image formation system equations to accurately localize the highly curved lumen boundaries. This approach avoids the need for image segmentation, which may reduce the localization accuracy due to spatial discretization. We demonstrate that the model parameters can be reliably identified by a feedforward neural network which, driven by the cross-section images, predicts the parameter values many times faster than a reference least-squares (LS) model fitting algorithm. We present and discuss two example applications, modeling the lower extremities of artery–vein complexes visualized in steady-state contrast-enhanced magnetic resonance images (MRI) and the coronary arteries pictured in computed tomography angiograms (CTA). Beyond applications in medical diagnosis, blood-flow simulation and vessel-phantom design, the method can serve as a tool for automated annotation of image datasets to train machine-learning algorithms. Full article
(This article belongs to the Special Issue Biomedical Imaging: Present and Future Challenges, from Image Processing Sensors through Artificial Intelligence)
►▼ Show Figures

Figure 1

Review

Jump to: Research

58 pages, 12032 KiB  
Review
Artificial Intelligence in Pancreatic Image Analysis: A Review
by Weixuan Liu, Bairui Zhang, Tao Liu, Juntao Jiang and Yong Liu
Sensors 2024, 24(14), 4749; https://doi.org/10.3390/s24144749 - 22 Jul 2024
Viewed by 2002
Abstract
Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing [...] Read more.
Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel’s workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms. Full article
(This article belongs to the Special Issue Biomedical Imaging: Present and Future Challenges, from Image Processing Sensors through Artificial Intelligence)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop