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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: closed (20 February 2025) | Viewed by 13492

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.

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Published Papers (8 papers)

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Research

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18 pages, 3263 KiB  
Article
Boosting Skin Cancer Classification: A Multi-Scale Attention and Ensemble Approach with Vision Transformers
by Guang Yang, Suhuai Luo and Peter Greer
Sensors 2025, 25(8), 2479; https://doi.org/10.3390/s25082479 - 15 Apr 2025
Viewed by 318
Abstract
Skin cancer is a significant global health concern, with melanoma being the most dangerous form, responsible for the majority of skin cancer-related deaths. Early detection of skin cancer is critical, as it can drastically improve survival rates. While deep learning models have achieved [...] Read more.
Skin cancer is a significant global health concern, with melanoma being the most dangerous form, responsible for the majority of skin cancer-related deaths. Early detection of skin cancer is critical, as it can drastically improve survival rates. While deep learning models have achieved impressive results in skin cancer classification, there remain challenges in accurately distinguishing between benign and malignant lesions. In this study, we introduce a novel multi-scale attention-based performance booster inspired by the Vision Transformer (ViT) architecture, which enhances the accuracy of both ViT and convolutional neural network (CNN) models. By leveraging attention maps to identify discriminative regions within skin lesion images, our method improves the models’ focus on diagnostically relevant areas. Additionally, we employ ensemble learning techniques to combine the outputs of several deep learning models using majority voting. Our skin cancer classifier, consisting of ViT and EfficientNet models, achieved a classification accuracy of 95.05% on the ISIC2018 dataset, outperforming individual models. The results demonstrate the effectiveness of integrating attention-based multi-scale learning and ensemble methods in skin cancer classification. Full article
(This article belongs to the Special Issue Biomedical Imaging: Present and Future Challenges, from Image Processing Sensors through Artificial Intelligence)
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23 pages, 4690 KiB  
Article
DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN–Transformer Network
by Yinan Cai, Zhao Meng and Dian Huang
Sensors 2025, 25(1), 231; https://doi.org/10.3390/s25010231 - 3 Jan 2025
Cited by 2 | Viewed by 1618
Abstract
Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, [...] Read more.
Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods. Most existing methods mainly focus on identifying the characteristics of clean EEG signals to facilitate artifact removal; however, the potential to integrate cross-disciplinary knowledge, such as insights from artifact research, remains an area that requires further exploration. In this study, we developed DHCT-GAN, a new EEG denoising model, using a dual-branch hybrid network architecture. This model independently learns features from both clean EEG signals and artifact signals, then fuses this information through an adaptive gating network to generate denoised EEG signals that accurately preserve EEG signal features while effectively removing artifacts. We evaluated DHCT-GAN’s performance through waveform analysis, power spectral density (PSD) analysis, and six performance metrics. The results demonstrate that DHCT-GAN significantly outperforms recent state-of-the-art networks in removing various artifacts. Furthermore, ablation experiments revealed that the hybrid model surpasses single-branch models in artifact removal, underscoring the crucial role of artifact knowledge constraints in improving denoising effectiveness. Full article
(This article belongs to the Special Issue Biomedical Imaging: Present and Future Challenges, from Image Processing Sensors through Artificial Intelligence)
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20 pages, 1470 KiB  
Article
Automatic Optical Path Alignment Method for Optical Biological Microscope
by Guojin Peng, Zhenming Yu, Xinjian Zhou, Guangyao Pang and Kuikui Wang
Sensors 2025, 25(1), 102; https://doi.org/10.3390/s25010102 - 27 Dec 2024
Viewed by 731
Abstract
A high-quality optical path alignment is essential for achieving superior image quality in optical biological microscope (OBM) systems. The traditional automatic alignment methods for OBMs rely heavily on complex masker-detection techniques. This paper introduces an innovative, image-sensor-based optical path alignment approach designed for [...] Read more.
A high-quality optical path alignment is essential for achieving superior image quality in optical biological microscope (OBM) systems. The traditional automatic alignment methods for OBMs rely heavily on complex masker-detection techniques. This paper introduces an innovative, image-sensor-based optical path alignment approach designed for low-power objective (specifically 4×) automatic OBMs. The proposed method encompasses reference objective (RO) identification and alignment processes. For identification, a model depicting spot movement with objective rotation near the optical axis is developed, elucidating the influence of optical path parameters on spot characteristics. This insight leads to the proposal of an RO identification method utilizing an edge gradient and edge position probability. In the alignment phase, a symmetry-based weight distribution scheme for concentric arcs is introduced. A significant observation is that the received energy stabilizes with improved alignment precision, prompting the design of an advanced alignment evaluation method that surpasses conventional energy-based assessments. The experimental results confirm that the proposed RO identification method can effectively differentiate between 4× and 10× objectives across diverse light intensities and exposure levels, with a significant numerical difference of up to 100. The error–radius ratio of the weighted circular fitting method is maintained below 1.16%, and the fine alignment stage’s evaluation curve is notably sharper. Moreover, tests under various imaging conditions in artificially saturated environments indicate that the alignment estimation method, predicated on critical saturation positions, achieves an average error of 0.875 pixels. Full article
(This article belongs to the Special Issue Biomedical Imaging: Present and Future Challenges, from Image Processing Sensors through Artificial Intelligence)
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33 pages, 8104 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 993
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)
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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 1451
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)
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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 2 | Viewed by 2389
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)
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Review

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21 pages, 1368 KiB  
Review
Advancing Platelet Research Through Live-Cell Imaging: Challenges, Techniques, and Insights
by Yuping Yolanda Tan, Jinghan Liu and Qian Peter Su
Sensors 2025, 25(2), 491; https://doi.org/10.3390/s25020491 - 16 Jan 2025
Viewed by 1031
Abstract
Platelet cells are essential to maintain haemostasis and play a critical role in thrombosis. They swiftly respond to vascular injury by adhering to damaged vessel surfaces, activating signalling pathways, and aggregating with each other to control bleeding. This dynamic process of platelet activation [...] Read more.
Platelet cells are essential to maintain haemostasis and play a critical role in thrombosis. They swiftly respond to vascular injury by adhering to damaged vessel surfaces, activating signalling pathways, and aggregating with each other to control bleeding. This dynamic process of platelet activation is intricately coordinated, spanning from membrane receptor maturation to intracellular interactions to whole-cell responses. Live-cell imaging has become an invaluable tool for dissecting these complexes. Despite its benefits, live imaging of platelets presents significant technical challenges. This review addresses these challenges, identifying key areas in need of further development and proposing possible solutions. We also focus on the dynamic processes of platelet adhesion, activation, and aggregation in haemostasis and thrombosis, applying imaging capacities from the microscale to the nanoscale. By exploring various live imaging techniques, we demonstrate how these approaches offer crucial insights into platelet biology and deepen our understanding of these three core events. In conclusion, this review provides an overview of the imaging methods currently available for studying platelet dynamics, guiding researchers in selecting suitable techniques for specific studies. By advancing our knowledge of platelet behaviour, these imaging methods contribute to research on haemostasis, thrombosis, and platelet-related diseases, ultimately aiming to improve clinical outcomes. Full article
(This article belongs to the Special Issue Biomedical Imaging: Present and Future Challenges, from Image Processing Sensors through Artificial Intelligence)
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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
Cited by 3 | Viewed by 3963
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)
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