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Algorithms
Special Issues
Advances in Intelligence Artificial Algorithms Applied to Medical Imaging
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Advances in Intelligence Artificial Algorithms Applied to Medical Imaging

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 11420

Special Issue Editors

Dr. Christian Mata

E-Mail Website
Guest Editor
Automatic Control Department (ESAII), Barcelona East School of Engineering (EEBE), Universitat Politècnica de Catalunya (UPC), 08019 Barcelona, Spain
Interests: computed imaging; computer science; computer vision and image processing; data acquisition programming; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Gilberto Ochoa-Ruiz

E-Mail Website
Co-Guest Editor
School of Engineering and Sciences,Tecnológico de Monterrey, Guadalajara 45201, Mexico
Interests: reconfigurable computing; smart cameras; edge computing; computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Raul Benitez

E-Mail Website
Co-Guest Editor
Automatic Control Department and Biomedical Engineering Research Center, Universitat Politècnica de Catalunya, 08028 Barcelona, Spain
Interests: pattern recognition; computer vision; biomedical image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Algorithms (ISSN 1999-4893) is currently running a Special Issue on medical imaging entitled “Advances in Artificial Intelligence Algorithms Applied to Medical Imaging”. Dr. Christian Mata is Guest Editor and Dr. Gilberto Ochoa-Ruiz and Dr. Raul Benítez are serving as Co-Guest Editors for this Special Issue. Based on your expertise and previous research, we believe that you could make an excellent contribution to this Special Issue.

Artificial intelligence algorithms have gained momentum in the scientific community. In the medical domain, advances in the implementation of new tools based on deep learning models or methodologies have become crucial to solving multiple medical issues. These tools are mainly related to problems involving techniques such as classification, segmentation, registration, and interpretability. Given their importance to the medical domain, their efficient solution is of paramount importance, either for research or in real practice. On the other hand, most of the problems in this field have significant computational complexity.

With this Special Issue, we aim to disseminate the latest findings and research achievements using artificial intelligence algorithms in the medical imaging domain. To this end, scholars and researchers from academia and the medical domain are invited to submit high-quality original contributions to this Special Issue.

Dr. Christian Mata Miquel
Dr. Gilberto Ochoa-Ruiz
Prof. Dr. Raul Benitez
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. Algorithms 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 1600 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 algorithms for computer-aided diagnosis
  • medical imaging and image analysis
  • deep learning and machine learning methods
  • segmentation and registration
  • data science for medical applications
  • dimensionality reduction and visualization techniques
  • explainability and interpretability

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Published Papers (3 papers)

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18 pages, 7014 KiB  
Article
Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks
by Ezequiel de la Rosa, Désiré Sidibé, Thomas Decourselle, Thibault Leclercq, Alexandre Cochet and Alain Lalande
Algorithms 2021, 14(8), 249; https://doi.org/10.3390/a14080249 - 20 Aug 2021
Cited by 11 | Viewed by 3203
Abstract
Late gadolinium enhancement (LGE) MRI is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard to quantify myocardial infarction (MI). Moreover, commercial software used in clinical practice are mostly semi-automatic, and [...] Read more.
Late gadolinium enhancement (LGE) MRI is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard to quantify myocardial infarction (MI). Moreover, commercial software used in clinical practice are mostly semi-automatic, and hence require direct intervention of experts. In this work, a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular obstruction areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of very light CNNs. We tested the method on a LGE-MRI database with healthy (n = 20) and diseased (n = 80) cases following a 5-fold cross-validation scheme. Our approach segmented myocardial scars with an average Dice coefficient of 77.22 ± 14.3% and with a volumetric error of 1.0 ± 6.9 cm3. In a comparison against nine reference algorithms, the proposed method achieved the highest agreement in volumetric scar quantification with the expert delineations (p< 0.001 when compared to the other approaches). Moreover, it was able to reproduce the scar segmentation intra- and inter-rater variability. Our approach was shown to be a good first attempt towards automatic and accurate myocardial scar segmentation, although validation over larger LGE-MRI databases is needed. Full article
(This article belongs to the Special Issue Advances in Intelligence Artificial Algorithms Applied to Medical Imaging)
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10 pages, 573 KiB  
Article
Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?
by Youssef Skandarani, Pierre-Marc Jodoin and Alain Lalande
Algorithms 2021, 14(7), 212; https://doi.org/10.3390/a14070212 - 14 Jul 2021
Cited by 1 | Viewed by 2674
Abstract
Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, [...] Read more.
Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with different loss functions on expert and non-expert ground truth for cardiac cine–MRI segmentation. Evaluation was done with classic segmentation metrics (Dice index and Hausdorff distance) as well as clinical measurements, such as the ventricular ejection fractions and the myocardial mass. The results reveal that generalization performances of a segmentation neural network trained on non-expert ground truth data is, to all practical purposes, as good as that trained on expert ground truth data, particularly when the non-expert receives a decent level of training, highlighting an opportunity for the efficient and cost-effective creation of annotations for cardiac data sets. Full article
(This article belongs to the Special Issue Advances in Intelligence Artificial Algorithms Applied to Medical Imaging)
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24 pages, 7917 KiB  
Article
The Practicality of Deep Learning Algorithms in COVID-19 Detection: Application to Chest X-ray Images
by Abdulaziz Alorf
Algorithms 2021, 14(6), 183; https://doi.org/10.3390/a14060183 - 13 Jun 2021
Cited by 10 | Viewed by 3771
Abstract
Since January 2020, the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected the whole world, producing a respiratory disease that can become severe and even cause death in certain groups of people. The main method for diagnosing coronavirus disease 2019 [...] Read more.
Since January 2020, the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected the whole world, producing a respiratory disease that can become severe and even cause death in certain groups of people. The main method for diagnosing coronavirus disease 2019 (COVID-19) is performing viral tests. However, the kits for carrying out these tests are scarce in certain regions of the world. Lung conditions as perceived in computed tomography and radiography images exhibit a high correlation with the presence of COVID-19 infections. This work attempted to assess the feasibility of using convolutional neural networks for the analysis of pulmonary radiography images to distinguish COVID-19 infections from non-infected cases and other types of viral or bacterial pulmonary conditions. The results obtained indicate that these networks can successfully distinguish the pulmonary radiographies of COVID-19-infected patients from radiographies that exhibit other or no pathology, with a sensitivity of 100% and specificity of 97.6%. This could help future efforts to automate the process of identifying lung radiography images of suspicious cases, thereby supporting medical personnel when many patients need to be rapidly checked. The automated analysis of pulmonary radiography is not intended to be a substitute for formal viral tests or formal diagnosis by a properly trained physician but rather to assist with identification when the need arises. Full article
(This article belongs to the Special Issue Advances in Intelligence Artificial Algorithms Applied to Medical Imaging)
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