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Applied Sciences
Special Issues
Application of Machine Learning to Imaging
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Application of Machine Learning to Imaging

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

Deadline for manuscript submissions: closed (25 October 2022) | Viewed by 2129

Special Issue Editor

Dr. Nicolas Lori

E-Mail Website
Guest Editor
ALGORITMI Research Center, School of Engineering, Gualtar Campus, University of Minho, 4710-057 Braga, Portugal
Interests: advanced MRI data-processing; imaging neuroscience; quantum computing; Artificial Intelligence

Special Issue Information

Dear Colleagues,

Imaging (e.g., Magnetic Resonance Imaging) has provided very useful information for multiple fields of science, but the technological improvements associated with Imaging data-processing have seen very few practical applications (e.g., clinical applications). A possible facilitator of the use of advanced imaging data-processing technologies is Artificial Intelligence (AI), such as the use of machine learning running in Graphical Processing Units (GPUs). The goal of this publication is to publicize cases where advanced technologies of imaging data-processing have been used in practical settings. The most advanced imaging technique used in practical settings usually involves AI, and there are interesting recent developments of AI that involve the use of Ontologies to obtain Explainable AI (xAI), as well as other recent improvements.

Dr. Nicolas Lori
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. Applied Sciences 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 2400 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

  • imaging
  • artificial intelligence
  • graphical processing units

Published Papers (1 paper)

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Research

15 pages, 9709 KiB  
Article
Generation of Synthetic Rat Brain MRI Scans with a 3D Enhanced Alpha Generative Adversarial Network
by André Ferreira, Ricardo Magalhães, Sébastien Mériaux and Victor Alves
Appl. Sci. 2022, 12(10), 4844; https://0-doi-org.brum.beds.ac.uk/10.3390/app12104844 - 11 May 2022
Cited by 1 | Viewed by 1619
Abstract
Translational brain research using Magnetic Resonance Imaging (MRI) is becoming increasingly popular as animal models are an essential part of scientific studies and more ultra-high-field scanners are becoming available. Some disadvantages of MRI are the availability of MRI scanners and the time required [...] Read more.
Translational brain research using Magnetic Resonance Imaging (MRI) is becoming increasingly popular as animal models are an essential part of scientific studies and more ultra-high-field scanners are becoming available. Some disadvantages of MRI are the availability of MRI scanners and the time required for a full scanning session. Privacy laws and the 3Rs ethics rule also make it difficult to create large datasets for training deep learning models. To overcome these challenges, an adaptation of the alpha Generative Adversarial Networks (GANs) architecture was used to test its ability to generate realistic 3D MRI scans of the rat brain in silico. As far as the authors are aware, this was the first time a GAN-based approach was used to generate synthetic MRI data of the rat brain. The generated scans were evaluated using various quantitative metrics, a Turing test, and a segmentation test. The last two tests proved the realism and applicability of the generated scans to real problems. Therefore, by using the proposed new normalisation layer and loss functions, it was possible to improve the realism of the generated rat MRI scans, and it was shown that using the generated data improved the segmentation model more than using the conventional data augmentation. Full article
(This article belongs to the Special Issue Application of Machine Learning to Imaging)
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