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Bioengineering
Special Issues
Machine Learning-Aided Medical Image Analysis
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Machine Learning-Aided Medical Image Analysis

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2502

Special Issue Editors

Dr. Amirreza Mahbod

E-Mail Website
Guest Editor
Assistant Professor, Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Danube Private University, Krems an der Donau, Austria
Interests: artificial intelligence; machine learning; medical image analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ramona Woitek

E-Mail Website
Guest Editor
Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Danube Private University, Krems an der Donau, Austria
Interests: artificial intelligence; machine learning; medical image analysis

Special Issue Information

Dear Colleagues,

We are witnessing an unprecedented era in medical imaging, where machine learning (ML) plays a crucial role in transforming diagnostic methodologies, enhancing automated image analysis, and ultimately improving patient outcomes. The integration of ML into medical image analysis has opened new frontiers in identifying, classifying, and quantifying patterns in medical images.

The aim of this Special Issue, titled "Machine Learning-Aided Medical Image Analysis", is to showcase the latest advances in machine learning technologies that push the boundaries of medical image analysis. We invite contributions demonstrating the innovative use of machine learning approaches across various tasks in medical imaging. This includes, but is not limited to, image classification, semantic or instance segmentation, the development of interpretable artificial intelligence (AI)-based systems, radiomics-based image analysis, and the application of robust pre- and post-processing techniques to boost diagnostic accuracy and efficiency. Moreover, submissions exploring novel applications of machine learning in emerging imaging modalities, interdisciplinary studies combining ML with other fields (e.g., genomics, pathology), and studies focusing on the ethical considerations of using artificial intelligence in medical imaging are also welcome.

By bringing together the latest developments in machine learning innovations and the critical domain of medical image analysis, we look forward to contributions that ignite further research and pave the way for next-generation diagnostic and therapeutic techniques.

Dr. Amirreza Mahbod
Prof. Dr. Ramona Woitek
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. 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

  • artificial intelligence
  • deep learning
  • machine learning
  • medical image analysis
  • medical imaging

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Published Papers (2 papers)

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17 pages, 6488 KiB  
Article
FDoSR-Net: Frequency-Domain Informed Auto-Encoder Network for Arbitrary-Scale 3D Whole-Heart MRI Super-Resolution
by Corbin Maciel and Qing Zou
Bioengineering 2025, 12(2), 129; https://doi.org/10.3390/bioengineering12020129 - 30 Jan 2025
Viewed by 350
Abstract
This work aims to develop a three-dimensional (3D) super-resolution (SR) network that would perform arbitrary-scale 3D whole-heart (WH) magnetic resonance imaging (MRI) super-resolution, while maintaining fine image details. One-hundred-twenty 3D WH MR volumes, acquired using four different sequences, are used in this study [...] Read more.
This work aims to develop a three-dimensional (3D) super-resolution (SR) network that would perform arbitrary-scale 3D whole-heart (WH) magnetic resonance imaging (MRI) super-resolution, while maintaining fine image details. One-hundred-twenty 3D WH MR volumes, acquired using four different sequences, are used in this study for training, validation, and testing. The proposed method utilizes a frequency-domain regularization in training to maintain fine image detail along with a 3D autoencoder framework. It is also trained in manner to enable it to perform arbitrary factor SR. The proposed method is compared against multiple super-resolution algorithms including two state-of-the-art deep learning methods referred to here as ACNS and TFC as well as nearest neighbor interpolation. The proposed method was evaluated quantitatively and compared against the competing methods with the mean result of the proposed method and the improvements provided by the proposed method (reported by mean percentage between the proposed method and all other competing methods) were recorded. The metrics of interest used for the quantitative comparison are peak signal-to-noise ratio (PSNR, mean = 34.10, mean percentage of improvement = 4.5%), structural similarity index measure (SSIM, mean = 0.94, mean percentage of improvement = 2.2%), mean squared error (MSE, mean = 0.00094, mean percentage of improvement = 48.2%), and root mean squared error (RMSE, mean = 0.024, mean percentage of improvement = 31.0%). Moreover, qualitative comparison was performed using multiple visual comparisons. The quantitative results achieved demonstrate that the proposed method regularly outperforms all other comparison methods. The visual comparisons demonstrate that the proposed method outperforms current state-of-the-art methods in preserving fine image details, as well as its ability to do so for multiple SR factors. Full article
(This article belongs to the Special Issue Machine Learning-Aided Medical Image Analysis)
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26 pages, 2887 KiB  
Article
Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models
by Simon Johannes Joham, Arnela Hadzic and Martin Urschler
Bioengineering 2024, 11(9), 932; https://doi.org/10.3390/bioengineering11090932 - 17 Sep 2024
Viewed by 1486
Abstract
The task of localizing distinct anatomical structures in medical image data is an essential prerequisite for several medical applications, such as treatment planning in orthodontics, bone-age estimation, or initialization of segmentation methods in automated image analysis tools. Currently, Anatomical Landmark Localization (ALL) is [...] Read more.
The task of localizing distinct anatomical structures in medical image data is an essential prerequisite for several medical applications, such as treatment planning in orthodontics, bone-age estimation, or initialization of segmentation methods in automated image analysis tools. Currently, Anatomical Landmark Localization (ALL) is mainly solved by deep-learning methods, which cannot guarantee robust ALL predictions; there may always be outlier predictions that are far from their ground truth locations due to out-of-distribution inputs. However, these localization outliers are detrimental to the performance of subsequent medical applications that rely on ALL results. The current ALL literature relies heavily on implicit anatomical constraints built into the loss function and network architecture to reduce the risk of anatomically infeasible predictions. However, we argue that in medical imaging, where images are generally acquired in a controlled environment, we should use stronger explicit anatomical constraints to reduce the number of outliers as much as possible. Therefore, we propose the end-to-end trainable Global Anatomical Feasibility Filter and Analysis (GAFFA) method, which uses prior anatomical knowledge estimated from data to explicitly enforce anatomical constraints. GAFFA refines the initial localization results of a U-Net by approximately solving a Markov Random Field (MRF) with a single iteration of the sum-product algorithm in a differentiable manner. Our experiments demonstrate that GAFFA outperforms all other landmark refinement methods investigated in our framework. Moreover, we show that GAFFA is more robust to large outliers than state-of-the-art methods on the studied X-ray hand dataset. We further motivate this claim by visualizing the anatomical constraints used in GAFFA as spatial energy heatmaps, which allowed us to find an annotation error in the hand dataset not previously discussed in the literature. Full article
(This article belongs to the Special Issue Machine Learning-Aided Medical Image Analysis)
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