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Biomedical Imaging: Present and Future Challenges, from Image Processing Sensors Through Artificial Intelligence—2nd Edition

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

Deadline for manuscript submissions: closed (20 March 2026) | Viewed by 1059

Special Issue Editors


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Guest Editor
Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, 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

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Guest Editor
Department of Biomedical Sciences, Humanitas University, Milan, Italy
Interests: laser-induced luminescent techniques; optical spectroscopy; microscopy
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, and the application of artificial intelligence in healthcare
Special Issues, Collections and Topics in MDPI journals

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 increasingly 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. 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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

  • 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

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Published Papers (1 paper)

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Research

17 pages, 5241 KB  
Article
DSF-BRNet: Dual-Gated Semantic Fusion and Boundary Refinement for Efficient Endoscopic Polyp Segmentation
by Botao Liu, Changqi Shi and Ming Zhao
Sensors 2026, 26(9), 2717; https://doi.org/10.3390/s26092717 - 28 Apr 2026
Viewed by 416
Abstract
Early detection and accurate segmentation of colorectal polyps during colonoscopy are crucial for the prevention of colorectal cancer. However, automated polyp segmentation remains challenging because of high inter-class variance, complex intestinal backgrounds, and blurred boundaries. To address these issues while maintaining computational efficiency, [...] Read more.
Early detection and accurate segmentation of colorectal polyps during colonoscopy are crucial for the prevention of colorectal cancer. However, automated polyp segmentation remains challenging because of high inter-class variance, complex intestinal backgrounds, and blurred boundaries. To address these issues while maintaining computational efficiency, DSF-BRNet was developed for endoscopic polyp segmentation. In this framework, a Dual-Gated Semantic Fusion (DSF) module is introduced to reduce spatial misalignment between cross-level features and to provide a more reliable semantic basis for lesion localization. To further alleviate boundary ambiguity, a High-Frequency Boundary Refinement (HBR) module is used to sharpen segmentation contours under aligned semantic guidance. Together, these components form an Align-then-Refine framework in which semantic localization is strengthened before boundary refinement is performed. Experiments on four public benchmark datasets—Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB—showed competitive performance with favorable computational efficiency. Mean Dice scores of 0.943 on CVC-ClinicDB and 0.818 on ETIS-LaribPolypDB were achieved, with 25.55 M parameters and an inference speed of 80.08 FPS. These results indicate that accurate semantic localization and fine boundary preservation can be achieved simultaneously, suggesting that the method may be promising for real-time computer-aided diagnosis (CAD). Full article
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