Artificial Intelligence in Medical Image Processing and Segmentation, Third Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 424

Special Issue Editor


E-Mail Website
Guest Editor
Department of Physics & Computer Science, Wilfrid Laurier University, Waterloo, ON, Canada
Interests: medical imaging; image processing and quantification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, Artificial Intelligence (AI) has deeply revolutionized the field of medical image processing. In particular, image segmentation has benefited the most from such innovations.

This boost has led to great advancements in the translation of AI algorithms from solely laboratory-based use to real clinical practice, especially for assisting with computer-aided diagnosis and image-guided surgery.

We are pleased to invite you to submit your work to this Special Issue focused on the cutting-edge developments in AI applications in the medical imaging field.

Bioengineering will be accepting contributions (both original articles and reviews) centered primarily on the following topics:

  • Medical image segmentation;
  • AI-based medical image registration;
  • Medical image recognition;
  • Patient/treatment stratification based on AI image processing;
  • Human interactions for the improvement of AI image processing outcomes;
  • Image-guided surgery/radiotherapy based on AI;
  • Radiomics;
  • Explainable AI in medicine.

Dr. Bernard Chiu
Guest Editor

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. Bioengineering 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 2700 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

  • medical image processing
  • image segmentation
  • computer-aided diagnosis
  • image-guided surgery
  • artificial intelligence

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issues

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

26 pages, 25350 KB  
Article
Applying Supervised Machine Learning to Effusion Analysis for the Diagnosis of Feline Infectious Peritonitis
by Dawn E. Dunbar, Simon A. Babayan, Sarah Krumrie, Sharmila Rennie, Elspeth M. Waugh, Margaret J. Hosie and William Weir
Bioengineering 2026, 13(2), 127; https://doi.org/10.3390/bioengineering13020127 - 23 Jan 2026
Abstract
Feline infectious peritonitis (FIP) is a major disease of cats which, unless promptly diagnosed and treated, is invariably fatal. Although it has long been recognised that the condition is the result of an aberrant immune response to infection with feline coronavirus, there remain [...] Read more.
Feline infectious peritonitis (FIP) is a major disease of cats which, unless promptly diagnosed and treated, is invariably fatal. Although it has long been recognised that the condition is the result of an aberrant immune response to infection with feline coronavirus, there remain significant gaps in our understanding of its pathogenesis. Consequently, diagnosis is complex and relies on the combined interpretation of numerous clinical signs and laboratory biomarkers, many of which are non-specific. In the case of effusive FIP, a commonly encountered acute form of the disease where body cavity effusions develop; the interpretation of fluid analysis results is key to diagnosing the condition. We hypothesised that machine learning could be applied to fluid analysis test data in order to help diagnose effusive FIP. Thus, historical test records from a veterinary laboratory dataset of 718 suspected cases of effusive disease were identified, representing 336 cases of FIP and 382 cases that were determined not to be FIP. This dataset was used to train an ensemble model to predict disease status based on clinical observations and laboratory features. Our model predicts the correct disease state with an accuracy of 96.51%, an area under the receiver operator curve of 96.48%, a sensitivity of 98.85% and a specificity of 94.12%. This study demonstrates that machine learning can be successfully applied to the interpretation of fluid analysis results to accurately detect cases of effusive FIP. Thus, this method has the potential to be utilised in a veterinary diagnostic laboratory setting to standardise and improve service provision. Full article
Show Figures

Figure 1

Back to TopTop