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A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 22587

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Dr. Christian Mata

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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
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Dr. Raul Benitez

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Automatic Control Department and Biomedical Engineering Research Center, Universitat Politècnica de Catalunya, 08028 Barcelona, Spain
Interests: pattern recognition; computer vision; biomedical image processing

Dr. Gilberto Ochoa-Ruiz

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Guest Editor

School of Engineering and Sciences,Tecnológico de Monterrey, Guadalajara 45201, Mexico
Interests: reconfigurable computing; smart cameras; edge computing; computer vision; machine learning
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Special Issue Information

Dear Colleagues,

The journal Algorithms (ISSN 1999-4893) is currently running a Special Issue on medical imaging entitled "Artificial Intelligence in Medical Imaging". Dr. Christian Mata is the Guest Editor for this Special Issue and Dr. Gilberto Ochoa-Ruiz and Dr. Raul Benítez Iglesias are serving as Co-Guest Editors. We believe you could make an excellent contribution to this Special Issue, based on your expertise and previous works.

Artificial intelligence algorithms have gained momentum in the scientific community. In the medical domain, advances in the implementation of new tools based on artificial intelligence models, such as machine and deep learning, have become crucial to solve multiple medical issues. These tools are mainly related to problems that involve 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 and/or in real practice. On the other hand, most of the problems in this field have remarkable computational complexity.

This Special Issue aims at disseminating the latest findings and research achievements in the medical imaging domain using artificial intelligence algorithms. 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
Dr. Raul Benitez
Dr. Gilberto Ochoa-Ruiz
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
literature reviews based on artificial intelligence methods

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Published Papers (10 papers)

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Research

20 pages, 2140 KiB

Open AccessArticle

Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI

by François Legrand, Richard Macwan, Alain Lalande, Lisa Métairie and Thomas Decourselle

Algorithms 2024, 17(1), 10; https://doi.org/10.3390/a17010010 - 25 Dec 2023

Viewed by 2281

Abstract

Automated Cardiac Magnetic Resonance segmentation serves as a crucial tool for the evaluation of cardiac function, facilitating faster clinical assessments that prove advantageous for both practitioners and patients alike. Recent studies have predominantly concentrated on delineating structures on short-axis orientation, placing less emphasis on long-axis representations due to the intricate nature of structures in the latter. Taking these consideration into account, we present a robust hierarchy-based augmentation strategy coupled with the compact and fast Efficient-Net (ENet) architecture for the automated segmentation of two-chamber and four-chamber Cine-MRI images. We observed an average Dice improvement of 0.99% on the two-chamber images and of 2.15% on the four-chamber images, and an average Hausdorff distance improvement of 21.3% on the two-chamber images and of 29.6% on the four-chamber images. The practical viability of our approach was validated by computing clinical metrics such as the Left Ventricular Ejection Fraction (LVEF) and left ventricular volume (LVC). We observed acceptable biases, with a +2.81% deviation on the LVEF for the two-chamber images and a +0.11% deviation for the four-chamber images. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Imaging)

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16 pages, 2155 KiB

Open AccessArticle

Deep Learning Based on EfficientNet for Multiorgan Segmentation of Thoracic Structures on a 0.35 T MR-Linac Radiation Therapy System

by Mohammed Chekroun, Youssef Mourchid, Igor Bessières and Alain Lalande

Algorithms 2023, 16(12), 564; https://doi.org/10.3390/a16120564 - 12 Dec 2023

Viewed by 1814

Abstract

The advent of the 0.35 T MR-Linac (MRIdian, ViewRay) system in radiation therapy allows precise tumor targeting for moving lesions. However, the lack of an automatic volume segmentation function in the MR-Linac’s treatment planning system poses a challenge. In this paper, we propose a deep-learning-based multiorgan segmentation approach for the thoracic region, using EfficientNet as the backbone for the network architecture. The objectives of this approach include accurate segmentation of critical organs, such as the left and right lungs, the heart, the spinal cord, and the esophagus, essential for minimizing radiation toxicity during external radiation therapy. Our proposed approach, when evaluated on an internal dataset comprising 81 patients, demonstrated superior performance compared to other state-of-the-art methods. Specifically, the results for our approach with a 2.5D strategy were as follows: a dice similarity coefficient (DSC) of 0.820 ± 0.041, an intersection over union (IoU) of 0.725 ± 0.052, and a 3D Hausdorff distance (HD) of 10.353 ± 4.974 mm. Notably, the 2.5D strategy surpassed the 2D strategy in all three metrics, exhibiting higher DSC and IoU values, as well as lower HD values. This improvement strongly suggests that our proposed approach with the 2.5D strategy may hold promise in achieving more precise and accurate segmentations when compared to the conventional 2D strategy. Our work has practical implications in the improvement of treatment planning precision, aligning with the evolution of medical imaging and innovative strategies for multiorgan segmentation tasks. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Imaging)

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16 pages, 8874 KiB

Open AccessArticle

Automatic Segmentation of Histological Images of Mouse Brains

by Juan Cisneros, Alain Lalande, Binnaz Yalcin, Fabrice Meriaudeau and Stephan Collins

Algorithms 2023, 16(12), 553; https://doi.org/10.3390/a16120553 - 1 Dec 2023

Viewed by 2010

Abstract

Using a high-throughput neuroanatomical screen of histological brain sections developed in collaboration with the International Mouse Phenotyping Consortium, we previously reported a list of 198 genes whose inactivation leads to neuroanatomical phenotypes. To achieve this milestone, tens of thousands of hours of manual image segmentation were necessary. The present work involved developing a full pipeline to automate the application of deep learning methods for the automated segmentation of 24 anatomical regions used in the aforementioned screen. The dataset includes 2000 annotated parasagittal slides (24,000 × 14,000 pixels). Our approach consists of three main parts: the conversion of images (.ROI to .PNG), the training of the deep learning approach on the compressed images (512 × 256 and 2048 × 1024 pixels of the deep learning approach) to extract the regions of interest using either the U-Net or Attention U-Net architectures, and finally the transformation of the identified regions (.PNG to .ROI), enabling visualization and editing within the Fiji/ImageJ 1.54 software environment. With an image resolution of 2048 × 1024, the Attention U-Net provided the best results with an overall Dice Similarity Coefficient (DSC) of 0.90 ± 0.01 for all 24 regions. Using one command line, the end-user is now able to pre-analyze images automatically, then runs the existing analytical pipeline made of ImageJ macros to validate the automatically generated regions of interest resulting. Even for regions with low DSC, expert neuroanatomists rarely correct the results. We estimate a time savings of 6 to 10 times. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Imaging)

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14 pages, 4300 KiB

Open AccessArticle

Automatic Multiorgan Segmentation in Pelvic Region with Convolutional Neural Networks on 0.35 T MR-Linac Images

by Emmanouil Koutoulakis, Louis Marage, Emmanouil Markodimitrakis, Leone Aubignac, Catherine Jenny, Igor Bessieres and Alain Lalande

Algorithms 2023, 16(11), 521; https://doi.org/10.3390/a16110521 - 15 Nov 2023

Viewed by 1908

Abstract

MR-Linac is a recent device combining a linear accelerator with an MRI scanner. The improved soft tissue contrast of MR images is used for optimum delineation of tumors or organs at risk (OARs) and precise treatment delivery. Automatic segmentation of OARs can contribute to alleviating the time-consuming process for radiation oncologists and improving the accuracy of radiation delivery by providing faster, more consistent, and more accurate delineation of target structures and organs at risk. It can also help reduce inter-observer variability and improve the consistency of contouring while reducing the time required for treatment planning. In this work, state-of-the-art deep learning techniques were evaluated based on 2D and 2.5D training strategies to develop a comprehensive tool for the accurate segmentation of pelvic OARs dedicated to 0.35 T MR-Linac. In total, 103 cases with 0.35 T MR images of the pelvic region were investigated. Experts considered and contoured the bladder, rectum, and femoral heads as OARs and the prostate as the target volume. For the training of the neural network, 85 patients were randomly selected, and 18 were used for testing. Multiple U-Net-based architectures were considered, and the best model was compared using both 2D and 2.5D training strategies. The evaluation of the models was performed based on two metrics: the Dice similarity coefficient (DSC) and the Hausdorff distance (HD). In the 2D training strategy, Residual Attention U-Net (ResAttU-Net) had the highest scores among the other deep neural networks. Due to the additional contextual information, the configured 2.5D ResAttU-Net performed better. The overall DSC were 0.88 ± 0.09 and 0.86 ± 0.10, and the overall HD was 1.78 ± 3.02 mm and 5.90 ± 7.58 mm for 2.5D and 2D ResAttU-Net, respectively. The 2.5D ResAttU-Net provides accurate segmentation of OARs without affecting the computational cost. The developed end-to-end pipeline will be merged with the treatment planning system for in-time automatic segmentation. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Imaging)

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20 pages, 5506 KiB

Open AccessArticle

Comparison of Machine Learning Classifiers for the Detection of Breast Cancer in an Electrical Impedance Tomography Setup

by Jöran Rixen, Nico Blass, Simon Lyra and Steffen Leonhardt

Algorithms 2023, 16(11), 517; https://doi.org/10.3390/a16110517 - 13 Nov 2023

Cited by 1 | Viewed by 2018

Abstract

Breast cancer is the leading cause of cancer-related death among women. Early prediction is crucial as it severely increases the survival rate. Although classical X-ray mammography is an established technique for screening, many eligible women do not consider this due to concerns about pain from breast compression. Electrical Impedance Tomography (EIT) is a technique that aims to visualize the conductivity distribution within the human body. As cancer has a greater conductivity than surrounding fatty tissue, it provides a contrast for image reconstruction. However, the interpretation of EIT images is still hard, due to the low spatial resolution. In this paper, we investigated three different classification models for the detection of breast cancer. This is important as EIT is a highly non-linear inverse problem and tends to produce reconstruction artifacts, which can be misinterpreted as, e.g., tumors. To aid in the interpretation of breast cancer EIT images, we compare three different classification models for breast cancer. We found that random forests and support vector machines performed best for this task. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Imaging)

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26 pages, 5220 KiB

Open AccessArticle

Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces

by Alexey Kozin, Anton Gerasimov, Maxim Bakaev, Anton Pashkov and Olga Razumnikova

Algorithms 2023, 16(11), 502; https://doi.org/10.3390/a16110502 - 29 Oct 2023

Cited by 1 | Viewed by 1823

Abstract

Brain–computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) are inexpensive and do not require user training. However, the highly personalized reaction to visual stimulation is an obstacle to the wider application of this technique, as it can be ineffective, tiring, or even harmful at certain frequencies. In our experimental study, we proposed a new approach to the selection of optimal frequencies of photostimulation. By using a custom photostimulation device, we covered a frequency range from 5 to 25 Hz with 1 Hz increments, recording the subjects’ brainwave activity (EEG) and analyzing the signal-to-noise ratio (SNR) changes at the corresponding frequencies. The proposed set of SNR-based coefficients and the discomfort index, determined by the ratio of theta and beta rhythms in the EEG signal, enables the automation of obtaining the recommended stimulation frequencies for use in SSVEP-based BCIs. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Imaging)

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19 pages, 1864 KiB

Open AccessArticle

COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques

by Shubham Mathesul, Debabrata Swain, Santosh Kumar Satapathy, Ayush Rambhad, Biswaranjan Acharya, Vassilis C. Gerogiannis and Andreas Kanavos

Algorithms 2023, 16(10), 494; https://doi.org/10.3390/a16100494 - 23 Oct 2023

Cited by 8 | Viewed by 2895

Abstract

The COVID-19 pandemic has posed significant challenges in accurately diagnosing the disease, as severe cases may present symptoms similar to pneumonia. Real-Time Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) is the conventional diagnostic technique; however, it has limitations in terms of time-consuming laboratory procedures and kit availability. Radiological chest images, such as X-rays and Computed Tomography (CT) scans, have been essential in aiding the diagnosis process. In this research paper, we propose a deep learning (DL) approach based on Convolutional Neural Networks (CNNs) to enhance the detection of COVID-19 and its variants from chest X-ray images. Building upon the existing research in SARS and COVID-19 identification using AI and machine learning techniques, our DL model aims to extract the most significant features from the X-ray scans of affected individuals. By employing an explanatory CNN-based technique, we achieved a promising accuracy of up to

97 %

in detecting COVID-19 cases, which can assist physicians in effectively screening and identifying probable COVID-19 patients. This study highlights the potential of DL in medical imaging, specifically in detecting COVID-19 from radiological images. The improved accuracy of our model demonstrates its efficacy in aiding healthcare professionals and mitigating the spread of the disease. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Imaging)

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15 pages, 2594 KiB

Open AccessArticle

Automatic Myocardium Segmentation in Delayed-Enhancement MRI with Pathology-Specific Data Augmentation and Deep Learning Architectures

by Gonzalo E. Mosquera-Rojas, Cylia Ouadah, Azadeh Hadadi, Alain Lalande and Sarah Leclerc

Algorithms 2023, 16(10), 488; https://doi.org/10.3390/a16100488 - 20 Oct 2023

Viewed by 1868

Abstract

The extent of myocardial infarction (MI) can be evaluated thanks to delayed enhancement (DE) cardiac MRI. DE MRI is an imaging technique acquired several minutes after the injection of a contrast agent where MI appears with a bright signal. The automatic myocardium segmentation in DE MRI is quite challenging, especially when MI is present, since these areas usually showcase a heterogeneous aspect in terms of shape and intensity, thus obstructing the myocardium visibility. To overcome this issue, we propose an image processing-based data augmentation algorithm where diverse synthetic cases of MI were created in two different ways: fixed and adaptive. In the first one, the training set is enlarged by a specific factor, whereas in the second, the method receives feedback from the segmentation model during training and performs the augmentation exclusively on complex cases. The method performance was evaluated in single and multi-modality settings. In this latter, information from kinetic images (Cine MRI), which are acquired along DE MRI in the same examination, is also used, and the extracted features from both modalities are fused. The results show that applying the data augmentation in a fixed fashion on a multi-modality setting leads to a more consistent segmentation of the myocardium in DE MRI. The segmentation models, which were all UNet-based architectures, can better relate MI areas with the myocardium, thus increasing its overall robustness to pathology-specific local pattern perturbations. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Imaging)

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12 pages, 17141 KiB

Open AccessArticle

Development of a Mammography Calcification Detection Algorithm Using Deep Learning with Resolution-Preserved Image Patch Division

by Miu Sakaida, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa and Hiroyuki Sugimori

Algorithms 2023, 16(10), 483; https://doi.org/10.3390/a16100483 - 18 Oct 2023

Cited by 6 | Viewed by 2473

Abstract

Convolutional neural networks (CNNs) in deep learning have input pixel limitations, which leads to lost information regarding microcalcification when mammography images are compressed. Segmenting images into patches retains the original resolution when inputting them into the CNN and allows for identifying the location of calcification. This study aimed to develop a mammographic calcification detection method using deep learning by classifying the presence of calcification in the breast. Using publicly available data, 212 mammograms from 81 women were segmented into 224 × 224-pixel patches, producing 15,049 patches. These were visually classified for calcification and divided into five subsets for training and evaluation using fivefold cross-validation, ensuring image consistency. ResNet18, ResNet50, and ResNet101 were used for training, each of which created a two-class calcification classifier. The ResNet18 classifier achieved an overall accuracy of 96.0%, mammogram accuracy of 95.8%, an area under the curve (AUC) of 0.96, and a processing time of 0.07 s. The results of ResNet50 indicated 96.4% overall accuracy, 96.3% mammogram accuracy, an AUC of 0.96, and a processing time of 0.14 s. The results of ResNet101 indicated 96.3% overall accuracy, 96.1% mammogram accuracy, an AUC of 0.96, and a processing time of 0.20 s. This developed method offers quick, accurate calcification classification and efficient visualization of calcification locations. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Imaging)

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16 pages, 4794 KiB

Open AccessArticle

Explainable Artificial Intelligence Method (ParaNet+) Localises Abnormal Parathyroid Glands in Scintigraphic Scans of Patients with Primary Hyperparathyroidism

by Dimitris J. Apostolopoulos, Ioannis D. Apostolopoulos, Nikolaos D. Papathanasiou, Trifon Spyridonidis and George S. Panayiotakis

Algorithms 2023, 16(9), 435; https://doi.org/10.3390/a16090435 - 11 Sep 2023

Cited by 1 | Viewed by 1540

Abstract

The pre-operative localisation of abnormal parathyroid glands (PG) in parathyroid scintigraphy is essential for suggesting treatment and assisting surgery. Human experts examine the scintigraphic image outputs. An assisting diagnostic framework for localisation reduces the workload of physicians and can serve educational purposes. Former studies from the authors suggested a successful deep learning model, but it produced many false positives. Between 2010 and 2020, 648 participants were enrolled in the Department of Nuclear Medicine of the University Hospital of Patras, Greece. An innovative modification of the well-known VGG19 network (ParaNet+) is proposed to classify scintigraphic images into normal and abnormal classes. The Grad-CAM++ algorithm is applied to localise the abnormal PGs. An external dataset of 100 patients imaged at the same department who underwent parathyroidectomy in 2021 and 2022 was used for evaluation. ParaNet+ agreed with the human readers, showing 0.9861 on a patient-level and 0.8831 on a PG-level basis under a 10-fold cross-validation on the training set of 648 participants. Regarding the external dataset, the experts identified 93 of 100 abnormal patient cases and 99 of 118 surgically excised abnormal PGs. The human-reader false-positive rate (FPR) was 10% on a PG basis. ParaNet+ identified 99/100 abnormal cases and 103/118 PGs, with an 11.2% FPR. The model achieved higher sensitivity on both patient and PG bases than the human reader (99.0% vs. 93% and 87.3% vs. 83.9%, respectively), with comparable FPRs. Deep learning can assist in detecting and localising abnormal PGs in scintigraphic scans of patients with primary hyperparathyroidism and can be adapted to the everyday routine. Full article

(This article belongs to the Special Issue Artificial Intelligence for Medical Imaging)

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