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J. Imaging
Special Issues
Progress and Challenges in Biomedical Image Analysis
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Progress and Challenges in Biomedical Image Analysis

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: 30 December 2024 | Viewed by 2811

Special Issue Editors

Dr. Lei Li

E-Mail Website
Guest Editor
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
Interests: cardiac digital twins; medical image analysis; multi-modal AI
Dr. Zehor Belkhatir

E-Mail Website
Guest Editor
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
Interests: medical data analysis; robust and interpretable AI; radiomics

Special Issue Information

Dear Colleagues,

We are currently seeing a growing interest in the dynamic and rapidly evolving field of biomedical image analysis, which plays a vital role in a wide range of healthcare applications, ranging from diagnostics to identifying individualized health trends or treatment. With the development and progress that have been made in biomedical imaging technology, biomedical imaging has become an essential tool in daily medical diagnostics. In addition, transformational analytics tools, especially artificial intelligence (AI) techniques and capabilities, are being made more accessible to researchers and healthcare. This has led to medical image analysis becoming more and more important for both research and clinical medicine/healthcare communities. From traditional radiological imaging to cutting-edge techniques, this Special Issue seeks to create a platform for researchers to not only showcase their latest advancements but also share invaluable insights and collectively address challenges in biomedical image analysis through review papers. Beyond individual contributions, this Special Issue aspires to catalyze a transformative impact on digitization (including AI) in healthcare, with a particular focus on personalized medicine. 

By inviting authors to share their expertise and research findings, the initiative aims to shape the future landscape of biomedical image applications. The overarching goal is to create a knowledge-sharing hub that accelerates progress, fostering innovation in biomedical image analysis and ensuring its continued relevance in the broader context of the evolution of healthcare.

Dr. Lei Li
Dr. Zehor Belkhatir
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. Journal of Imaging is an international peer-reviewed open access monthly 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 1800 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

  • foundation models in medical imaging
  • digital twins
  • image-based personalized medicine
  • integration of imaging and non-imaging data (multi-modal analysis)
  • explainable and interpretable AI
  • radiomics analysis
  • image segmentation
  • image registration
  • image classification
  • advances in machine/deep learning (e.g., federated learning)
  • computer-aided diagnosis and surgery

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.
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  • 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 (2 papers)

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Research

12 pages, 1486 KiB  
Article
Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI
by Dayeong An and El-Sayed Ibrahim
J. Imaging 2024, 10(12), 308; https://doi.org/10.3390/jimaging10120308 - 1 Dec 2024
Viewed by 518
Abstract
Radiation therapy (RT) is widely used to treat thoracic cancers but carries a risk of radiation-induced heart disease (RIHD). This study aimed to detect early markers of RIHD using machine learning (ML) techniques and cardiac MRI in a rat model. SS.BN3 consomic rats, [...] Read more.
Radiation therapy (RT) is widely used to treat thoracic cancers but carries a risk of radiation-induced heart disease (RIHD). This study aimed to detect early markers of RIHD using machine learning (ML) techniques and cardiac MRI in a rat model. SS.BN3 consomic rats, which have a more subtle RIHD phenotype compared to Dahl salt-sensitive (SS) rats, were treated with localized cardiac RT or sham at 10 weeks of age. Cardiac MRI was performed 8 and 10 weeks post-treatment to assess global and regional cardiac function. ML algorithms were applied to differentiate sham-treated and irradiated rats based on early changes in myocardial function. Despite normal global left ventricular ejection fraction in both groups, strain analysis showed significant reductions in the anteroseptal and anterolateral segments of irradiated rats. Gradient boosting achieved an F1 score of 0.94 and an ROC value of 0.95, while random forest showed an accuracy of 88%. These findings suggest that ML, combined with cardiac MRI, can effectively detect early preclinical changes in RIHD, particularly alterations in regional myocardial contractility, highlighting the potential of these techniques for early detection and monitoring of radiation-induced cardiac dysfunction. Full article
(This article belongs to the Special Issue Progress and Challenges in Biomedical Image Analysis)
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16 pages, 1063 KiB  
Article
Quantitative Evaluation of White Matter Injury by Cranial Ultrasound to Detect the Effects of Parenteral Nutrition in Preterm Babies: An Observational Study
by Gianluigi Laccetta, Maria Chiara De Nardo, Raffaella Cellitti, Maria Di Chiara, Monica Tagliabracci, Pasquale Parisi, Flavia Gloria, Giuseppe Rizzo, Alberto Spalice and Gianluca Terrin
J. Imaging 2024, 10(9), 224; https://doi.org/10.3390/jimaging10090224 - 10 Sep 2024
Viewed by 1514
Abstract
Nutrition in early life has an impact on white matter (WM) development in preterm-born babies. Quantitative analysis of pixel brightness intensity (PBI) on cranial ultrasound (CUS) scans has shown a great potential in the evaluation of periventricular WM echogenicity in preterm newborns. We [...] Read more.
Nutrition in early life has an impact on white matter (WM) development in preterm-born babies. Quantitative analysis of pixel brightness intensity (PBI) on cranial ultrasound (CUS) scans has shown a great potential in the evaluation of periventricular WM echogenicity in preterm newborns. We aimed to investigate the employment of this technique to objectively verify the effects of parenteral nutrition (PN) on periventricular WM damage in preterm infants. Prospective observational study including newborns with gestational age at birth ≤32 weeks and/or birth weight ≤1500 g who underwent CUS examination at term-equivalent age. The echogenicity of parieto–occipital periventricular WM relative to that of homolateral choroid plexus (RECP) was calculated on parasagittal scans by means of quantitative analysis of PBI. Its relationship with nutrient intake through enteral and parenteral routes in the first postnatal week was evaluated. The study included 42 neonates for analysis. We demonstrated that energy and protein intake administered through the parenteral route positively correlated with both right and left RECP values (parenteral energy intake vs. right RECP: r = 0.413, p = 0.007; parenteral energy intake vs. left RECP: r = 0.422, p = 0.005; parenteral amino acid intake vs. right RECP: r = 0.438, p = 0.004; parenteral amino acid intake vs. left RECP: r = 0.446, p = 0.003). Multivariate linear regression analysis confirmed these findings. Quantitative assessment of PBI could be considered a simple, risk-free, and repeatable method to investigate the effects of PN on WM development in preterm neonates. Full article
(This article belongs to the Special Issue Progress and Challenges in Biomedical Image Analysis)
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