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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: 15 July 2024 | Viewed by 2166

Special Issue Editors


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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
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Guest Editor
Department of Biomedical Sciences, Humanitas University, Milan, Italy
Interests: laser-induced luminescent techniques; optical spectroscopy; microscopy

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Guest Editor
National Reseach Council of Italy (CNR), ISASI Institute of Applied Sciences & Intelligent Systems, 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

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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.

Published Papers (2 papers)

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Research

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 568
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
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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 881
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
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