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Applied Sciences
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Medical Image Processing, Reconstruction, and Visualization
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Medical Image Processing, Reconstruction, and Visualization

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 697

Special Issue Editors

Dr. Farid García-Lamont

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Guest Editor
Computer Engineering Department, Autonomous University of Mexico State, Toluca, Mexico
Interests: pattern recognition; robotics; artificial intelligence applications; image processing
Prof. Dr. Jair Cervantes

E-Mail Website
Guest Editor
Department of Computer Science, Autonomous University of Mexico State, Toluca, Mexico
Interests: algorithms; machine learning; classification; pattern recognition; feature extraction
Prof. Dr. Asdrúbal López Chau

E-Mail Website
Guest Editor
Centro Universitario UAEM Zumpango, Autonomous University of Mexico State, Toluca, Mexico
Interests: classification; advanced machine learning; pattern recognition; feature extraction; supervised learning; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Arturo Yee-Rendon

E-Mail Website
Guest Editor
Faculty of Information Technology, Autonomous University of Sinaloa, Culiacan, Mexico
Interests: pattern recognition; strategic analysis; classification algorithms; deep learning

Special Issue Information

Dear Colleagues,

With the advancement of technology and artificial intelligence, medical image processing algorithms have been developed to analyze images and assist physicians. According to the World Health Organization and the World Economic Forum, it is estimated that by 2030, there will be a shortage of 10 million healthcare professionals, including physicians. Therefore, it is very important to continue developing medical image processing algorithms. In order to automate the analysis processes performed by medical specialists and eliminate human intervention in the future, better algorithms are being developed all the time. Although these algorithms have achieved very high accuracy, these small percentages of errors still seriously impact diagnoses and analyses, as these errors can impact a person's health or even lead to death. This Special Issue presents the most recent advances in works addressing medical image processing and computer graphics applied to medicine.

Dr. Farid García-Lamont
Prof. Dr. Jair Cervantes
Prof. Dr. Asdrúbal López Chau
Dr. Arturo Yee-Rendon
Guest Editors

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Keywords

  • image processing
  • computer graphics
  • artificial intelligence
  • medical imaging

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Published Papers (1 paper)

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Research

17 pages, 9817 KB  
Article
SegMed: An Open-Source Desktop Tool for Deploying Pretrained Deep Learning Models in 3D Medical Image Segmentation
by Mhd Jafar Mortada, Agnese Sbrollini, Klaudia Proniewska-van Dam, Peter M. Van Dam and Laura Burattini
Appl. Sci. 2026, 16(7), 3490; https://doi.org/10.3390/app16073490 - 3 Apr 2026
Viewed by 399
Abstract
Deep learning has become central to semantic segmentation of three-dimensional medical images. However—despite many published models—their adoption in practice remains limited, as deployment often requires advanced programming skills and familiarity with specific machine learning frameworks. Thus, technical barriers restrict its use to specialized [...] Read more.
Deep learning has become central to semantic segmentation of three-dimensional medical images. However—despite many published models—their adoption in practice remains limited, as deployment often requires advanced programming skills and familiarity with specific machine learning frameworks. Thus, technical barriers restrict its use to specialized users. To address this, we present SegMed (version 1.0), an open-source, standalone desktop application that provides an end-to-end workflow for deep learning-based medical image segmentation. SegMed supports the loading and inspection of common medical image formats, as well as array-based formats. The application integrates standard preprocessing operations often used in the field and directly supports loading of pretrained segmentation models implemented in both PyTorch (version 2.X) and Keras (version 2.X) and those created using the Medical Open Network for AI framework (version 1.X). Models are automatically inspected to infer required configurations, such as input size and post-processing steps, enabling segmentation with minimal user intervention. Results can be exported as volumetric images or 3D surface meshes for downstream analysis, visualization, or special applications such as virtual reality. SegMed was tested using multiple publicly available pretrained models, demonstrating robustness and flexibility across diverse segmentation tasks. By abstracting low-level implementation details, SegMed lowers technical barriers, promotes reproducibility, and facilitates the integration of AI-assisted segmentation into medical imaging workflows. Full article
(This article belongs to the Special Issue Medical Image Processing, Reconstruction, and Visualization)
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