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Diagnostics
Special Issues
Medical Images Segmentation and Diagnosis
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Medical Images Segmentation and Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1225

Special Issue Editor

Dr. Steven L. Fernandes

E-Mail Website
Guest Editor
Department of Computer Science, Design and Journalism, Creighton University, Omaha, NE, USA
Interests: CT; machine learning; deep learning; tumor classification; medical images segmentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, "Medical Images Segmentation and Diagnosis," explores the latest medical image processing and analysis advancements. It highlights innovative techniques for image segmentation, diagnosis, and patient care, offering insights into the future of medical imaging. Contributors share their expertise and experiences, making this Issue a valuable resource for researchers and practitioners alike.

Dr. Steven L. Fernandes
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 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. Diagnostics 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

  • medical images
  • segmentation
  • diagnosis
  • prognosis
  • AI

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Published Papers (2 papers)

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Research

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17 pages, 2051 KiB  
Article
Lightweight Evolving U-Net for Next-Generation Biomedical Imaging
by Furkat Safarov, Ugiloy Khojamuratova, Misirov Komoliddin, Ziyat Kurbanov, Abdibayeva Tamara, Ishonkulov Nizamjon, Shakhnoza Muksimova and Young Im Cho
Diagnostics 2025, 15(9), 1120; https://doi.org/10.3390/diagnostics15091120 - 28 Apr 2025
Viewed by 121
Abstract
Background/Objectives: Accurate and efficient segmentation of cell nuclei in biomedical images is critical for a wide range of clinical and research applications, including cancer diagnostics, histopathological analysis, and therapeutic monitoring. Although U-Net and its variants have achieved notable success in medical image [...] Read more.
Background/Objectives: Accurate and efficient segmentation of cell nuclei in biomedical images is critical for a wide range of clinical and research applications, including cancer diagnostics, histopathological analysis, and therapeutic monitoring. Although U-Net and its variants have achieved notable success in medical image segmentation, challenges persist in balancing segmentation accuracy with computational efficiency, especially when dealing with large-scale datasets and resource-limited clinical settings. This study aims to develop a lightweight and scalable U-Net-based architecture that enhances segmentation performance while substantially reducing computational overhead. Methods: We propose a novel evolving U-Net architecture that integrates multi-scale feature extraction, depthwise separable convolutions, residual connections, and attention mechanisms to improve segmentation robustness across diverse imaging conditions. Additionally, we incorporate channel reduction and expansion strategies inspired by ShuffleNet to minimize model parameters without sacrificing precision. The model performance was extensively validated using the 2018 Data Science Bowl dataset. Results: Experimental evaluation demonstrates that the proposed model achieves a Dice Similarity Coefficient (DSC) of 0.95 and an accuracy of 0.94, surpassing state-of-the-art benchmarks. The model effectively delineates complex and overlapping nuclei structures with high fidelity, while maintaining computational efficiency suitable for real-time applications. Conclusions: The proposed lightweight U-Net variant offers a scalable and adaptable solution for biomedical image segmentation tasks. Its strong performance in both accuracy and efficiency highlights its potential for deployment in clinical diagnostics and large-scale biological research, paving the way for real-time and resource-conscious imaging solutions. Full article
(This article belongs to the Special Issue Medical Images Segmentation and Diagnosis)
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16 pages, 4546 KiB  
Technical Note
Demonstrating an Academic Core Facility for Automated Medical Image Processing and Analysis: Workflow Design and Practical Applications
by Yogesh Kumar, Rex A. Cardan, Ho-hsin Chang, Katherine A. Heinzman, Kadir Gultekin, Amy Goss, Andrew McDonald, Donna Murdaugh, Jonathan McConathy, Steven Rothenberg, Andrew D. Smith, John Fiveash and Carlos E. Cardenas
Diagnostics 2025, 15(7), 803; https://doi.org/10.3390/diagnostics15070803 - 21 Mar 2025
Viewed by 438
Abstract
Background/Objectives: Medical research institutions are increasingly leveraging artificial intelligence (AI) to enhance the processing and analysis of medical imaging data. However, scaling AI-driven medical image analysis often requires specialized expertise and infrastructure that individual labs may lack. A centralized solution is to establish [...] Read more.
Background/Objectives: Medical research institutions are increasingly leveraging artificial intelligence (AI) to enhance the processing and analysis of medical imaging data. However, scaling AI-driven medical image analysis often requires specialized expertise and infrastructure that individual labs may lack. A centralized solution is to establish a core facility—a shared institutional resource—dedicated to Automated Medical Image Processing and Analysis (AMIPA). Methods: This technical note offers a practical roadmap for institutions to create an AI-based core facility for AMIPA, drawing on our experience in building such a resource. Results: We outline the key components for replicating a successful AMIPA core facility, including high-performance computing resources, robust AI software pipelines, data management strategies, and dedicated support personnel. Emphasis is placed on workflow automation and reproducibility, ensuring researchers can efficiently and consistently process large imaging datasets. Conclusions: By following this roadmap, institutions can accelerate AI adoption in imaging workflows and foster a shared resource that enhances the quality and productivity of medical imaging research. Full article
(This article belongs to the Special Issue Medical Images Segmentation and Diagnosis)
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