Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.9
2.7
Journals
J. Imaging
Special Issues
Progress and Challenges in Biomedical Image Analysis
share announcement

Progress and Challenges in Biomedical Image Analysis

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

Deadline for manuscript submissions: closed (30 December 2024) | Viewed by 5353

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.
  • 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 policies can be found here.

Published Papers (5 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

16 pages, 1772 KiB  
Article
We Need to Talk About Lung Ultrasound Score: Prediction of Intensive Care Unit Admission with Machine Learning
by Duarte Oliveira-Saraiva, João Leote, Filipe André Gonzalez, Nuno Cruz Garcia and Hugo Alexandre Ferreira
J. Imaging 2025, 11(2), 45; https://doi.org/10.3390/jimaging11020045 - 7 Feb 2025
Viewed by 544
Abstract
The admission of COVID-19 patients to the Intensive Care Unit (ICU) is largely dependent on illness severity, yet no standard criteria exist for this decision. Here, lung ultrasound (LU) data, blood gas analysis (BGA), and clinical parameters from venous blood tests (VBTs) were [...] Read more.
The admission of COVID-19 patients to the Intensive Care Unit (ICU) is largely dependent on illness severity, yet no standard criteria exist for this decision. Here, lung ultrasound (LU) data, blood gas analysis (BGA), and clinical parameters from venous blood tests (VBTs) were used, along with machine-learning (ML) models to predict the need for ICU admission. Data from fifty-one COVID-19 patients, including ICU admission status, were collected. The information from LU was gathered through the identification of LU findings (LUFs): B-lines, irregular pleura, subpleural, and lobar consolidations. LU scores (LUSs) were computed by summing predefined weights assigned to each LUF, as reported in previous studies. In addition, individual LUFs were analyzed without calculating a total LUS. Support vector machine models were built, combining the available clinical data to predict ICU admissions. The application of ML models to individual LUFs outperformed standard LUS approaches reported in previous studies. Moreover, combining LU data with results from other medical exams improved the area under the receiver operating characteristic curve (AUC). The model with the best overall performance used variables from all three exams (BGA, LU, VBT), achieving an AUC of 95.5%. Overall, the results demonstrate the significant role of ML models in improving the prediction of ICU admission. Additionally, applying ML specifically to LUFs provided better results compared to traditional approaches that rely on traditional LUSs. The results of this paper are deployed on a web app. Full article
(This article belongs to the Special Issue Progress and Challenges in Biomedical Image Analysis)
►▼ Show Figures

Figure 1

16 pages, 4076 KiB  
Article
Imaging and Image Processing Techniques for High-Resolution Visualization of Connective Tissue with MRI: Application to Fascia, Aponeurosis, and Tendon
by Meeghage Randika Perera, Graeme M. Bydder, Samantha J. Holdsworth and Geoffrey G. Handsfield
J. Imaging 2025, 11(2), 43; https://doi.org/10.3390/jimaging11020043 - 4 Feb 2025
Viewed by 687
Abstract
Recent interest in musculoskeletal connective tissues like tendons, aponeurosis, and deep fascia has led to a greater focus on in vivo medical imaging, particularly MRI. Given the rapid T2* decay of collagenous tissues, advanced ultra-short echo time (UTE) MRI sequences have [...] Read more.
Recent interest in musculoskeletal connective tissues like tendons, aponeurosis, and deep fascia has led to a greater focus on in vivo medical imaging, particularly MRI. Given the rapid T2* decay of collagenous tissues, advanced ultra-short echo time (UTE) MRI sequences have proven useful in generating high-signal images of these tissues. To further these advances, we discuss the integration of UTE with Diffusion Tensor Imaging (DTI) and explore image processing techniques to enhance the localization, labeling, and modeling of connective tissues. These techniques are especially valuable for extracting features from thin tissues that may be difficult to distinguish. We present data from lower leg scans of 30 healthy subjects using a non-Cartesian MRI sequence to acquire axial 2D images to segment skeletal muscle and connective tissue. DTI helped differentiate aponeurosis from deep fascia by analyzing muscle fiber orientations. The dual echo imaging methods yielded high-resolution images of deep fascia, where in-plane spatial resolutions were between 0.3 × 0.3 mm to 0.5 × 0.5 mm with a slice thickness of 3–5 mm. Techniques such as K-Means clustering, FFT edge detection, and region-specific scaling were most effective in enhancing images of deep fascia, aponeurosis, and tendon to enable high-fidelity modeling of these tissues. Full article
(This article belongs to the Special Issue Progress and Challenges in Biomedical Image Analysis)
►▼ Show Figures

Figure 1

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 916
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)
►▼ Show Figures

Figure 1

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 1792
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)
►▼ Show Figures

Figure 1

Review

Jump to: Research

15 pages, 4634 KiB  
Review
Shaping the Optimal Timing for Treatment of Isolated Asymptomatic Severe Aortic Stenosis with Preserved Left Ventricular Ejection Fraction: The Role of Non-Invasive Diagnostics Focused on Strain Echocardiography and Future Perspectives
by Luca Dell’Angela and Gian Luigi Nicolosi
J. Imaging 2025, 11(2), 48; https://doi.org/10.3390/jimaging11020048 - 8 Feb 2025
Viewed by 396
Abstract
The optimal timing for treatment of patients with isolated asymptomatic severe aortic stenosis and preserved left ventricular ejection fraction is still controversial and research is ongoing. Once a diagnosis has been performed and other cardiac comorbidities (e.g., concomitant significant valvulopathies or infiltrative cardiomyopathies) [...] Read more.
The optimal timing for treatment of patients with isolated asymptomatic severe aortic stenosis and preserved left ventricular ejection fraction is still controversial and research is ongoing. Once a diagnosis has been performed and other cardiac comorbidities (e.g., concomitant significant valvulopathies or infiltrative cardiomyopathies) have reasonably been excluded, a hot topic is adequate myocardial characterization, which aims to prevent both myocardial dysfunction and subsequent adverse myocardial remodeling, and can potentially compromise the post-treatment outcomes. Another crucial subject of debate is the assessment of the real “preserved” left ventricular ejection fraction cut-off value in the presence of isolated asymptomatic severe aortic stenosis, in order to optimize the timing of aortic valve replacement as well. The aim of the present critical narrative review is highlighting the current role of non-invasive diagnostics in such a setting, focusing on strain echocardiography, and citing the main complementary cardiac imaging techniques, as well as suggesting potential implementation strategies in routine clinical practice in view of future developments. Full article
(This article belongs to the Special Issue Progress and Challenges in Biomedical Image Analysis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop