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Diagnostics
Special Issues
Advances in Functional and Structural MR Image Analysis
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Advances in Functional and Structural MR Image Analysis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 561

Special Issue Editor

Dr. Xingfeng Li

E-Mail Website
Guest Editor
Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
Interests: fMRI; image analysis; statistical analysis; cancer; brain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Thanks to technological developments in recent years, MR image analysis methods have made significant progress. For functional MRI (fMRI), different sequences have been proposed to acquire data in order to quantitatively measure signal changes. Quantitative MRI (qMRI) methods have been applied to cancer detection as well as in brain and whole-body MRI. The most used qMRI approaches include diffusion, perfusion, T1/T2/T2*, and proton maps. The methods used for the analysis of these images have a direct impact on the experimental design of cognitive, neuroscience, clinical diagnostic, and treatment studies.

With the recent advances in artificial intelligence, particularly deep learning methods, structural MRI analysis has made tremendous progress. For instance, U-net, Transformer, and Autocoder network structures have been suggested for MRI analysis, making it possible to develop the Segment Anything model. Numerous machine learning algorithms and statistical methods, including survival analysis, have been implemented to merge clinical and radiomic features from structural MRI for the prediction of clinical variables.

This Special Issue of the journal Diagnostics covers the methodologies for the analysis of functional and structural MR images, and its application to clinical studies. We invite researchers to share knowledge in this field so that patients can have better and more efficient treatment outcomes.

Dr. Xingfeng Li
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

  • functional MRI
  • structural MRI
  • image analysis
  • radiomics
  • machine learning

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Published Papers (1 paper)

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Research

21 pages, 4721 KB  
Article
Automated Brain Tumor MRI Segmentation Using ARU-Net with Residual-Attention Modules
by Erdal Özbay and Feyza Altunbey Özbay
Diagnostics 2025, 15(18), 2326; https://doi.org/10.3390/diagnostics15182326 - 13 Sep 2025
Viewed by 378
Abstract
Background/Objectives: Accurate segmentation of brain tumors in Magnetic Resonance Imaging (MRI) scans is critical for diagnosis and treatment planning due to their life-threatening nature. This study aims to develop a robust and automated method capable of precisely delineating heterogeneous tumor regions while improving [...] Read more.
Background/Objectives: Accurate segmentation of brain tumors in Magnetic Resonance Imaging (MRI) scans is critical for diagnosis and treatment planning due to their life-threatening nature. This study aims to develop a robust and automated method capable of precisely delineating heterogeneous tumor regions while improving segmentation accuracy and generalization. Methods: We propose Attention Res-UNet (ARU-Net), a novel Deep Learning (DL) architecture integrating residual connections, Adaptive Channel Attention (ACA), and Dimensional-space Triplet Attention (DTA) modules. The encoding module efficiently extracts and refines relevant feature information by applying ACA to the lower layers of convolutional and residual blocks. The DTA is fixed to the upper layers of the decoding module, decoupling channel weights to better extract and fuse multi-scale features, enhancing both performance and efficiency. Input MRI images are pre-processed using Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, denoising filters, and Linear Kuwahara filtering to preserve edges while smoothing homogeneous regions. The network is trained using categorical cross-entropy loss with the Adam optimizer on the BTMRII dataset, and comparative experiments are conducted against baseline U-Net, DenseNet121, and Xception models. Performance is evaluated using accuracy, precision, recall, F1-score, Dice Similarity Coefficient (DSC), and Intersection over Union (IoU) metrics. Results: Baseline U-Net showed significant performance gains after adding residual connections and ACA modules, with DSC improving by approximately 3.3%, accuracy by 3.2%, IoU by 7.7%, and F1-score by 3.3%. ARU-Net further enhanced segmentation performance, achieving 98.3% accuracy, 98.1% DSC, 96.3% IoU, and a superior F1-score, representing additional improvements of 1.1–2.0% over the U-Net + Residual + ACA variant. Visualizations confirmed smoother boundaries and more precise tumor contours across all six tumor classes, highlighting ARU-Net’s ability to capture heterogeneous tumor structures and fine structural details more effectively than both baseline U-Net and other conventional DL models. Conclusions: ARU-Net, combined with an effective pre-processing strategy, provides a highly reliable and precise solution for automated brain tumor segmentation. Its improvements across multiple evaluation metrics over U-Net and other conventional models highlight its potential for clinical application and contribute novel insights to medical image analysis research. Full article
(This article belongs to the Special Issue Advances in Functional and Structural MR Image Analysis)
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