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Diagnosis of Medical Imaging
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Diagnosis of Medical Imaging

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 7135

Special Issue Editors

Dr. Athanasios Koutras

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Guest Editor
Electrical & Computer Engineering Department, University of Peloponnese, Tripoli, Greece
Interests: digital sound processing and analysis; digital image processing and analysis; blind source and speech separation; biomedical signal processing and analysis; computer aided diagnosis systems; EEG/MEG brain signal analysis; brain computer interfaces; pattern recognition; machine learning; deep learning, artificial neural networks; music information retrieval; emotion recognition; time-series forecasting
Special Issues, Collections and Topics in MDPI journals
Dr. Dermatas Evangelos

E-Mail Website
Guest Editor
Department of Computer Engineering & Informatics, University of Patras, 26504 Patras, Greece
Interests: medical imaging; deep learning; breast cancer diagnosis; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Ioanna Christoyianni

E-Mail Website
Guest Editor
Electrical & Computer Engineering Department, University of Patras, 26504 Patras, Greece
Interests: medical image processing; breast cancer detection; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. George Apostolopoulos

E-Mail Website
Guest Editor
Electrical & Computer Engineering Department, University of Patras, 26504 Patras, Greece
Interests: medical imaging; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the years, medical imaging techniques such X-rays, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and positron emission tomography (PET) have revolutionized the way we diagnose and treat various medical conditions. These imaging modalities provide detailed insights into the human body, allowing healthcare professionals to identify diseases, monitor treatment progress, and guide surgical interventions.The aim of this Special Issue is to present the recent advances in medical imaging for detection and diagnosis, including through the use of machine learning and deep learning algorithms.

We especially invite submissions that utilize various Medical Imaging modalities such as digital mammography (DM), tomosynthesis, ultrasound, or MRI, to develop systems that assist in the diagnosis (CADx) and/or detection (CADe) of regions of interest in diseases. Submissions may also include, but are not limited to, innovative feature extraction techniques for disease detection and diagnosis, transfer learning and deep learning architectures, open-access databases for breast cancer research, generative adversarial network (GAN) architectures designed to address the challenges of small datasets.

The goal of this Special Issue is to explore our current standing and future possibilities within this crucial area of health-related research. We welcome submissions detailing new techniques, methods, applications, and results, as well as review articles.

Dr. Athanasios Koutras
Dr. Dermatas Evangelos
Dr. Ioanna Christoyianni
Dr. George Apostolopoulos
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. 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.

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Published Papers (7 papers)

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Research

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12 pages, 2564 KiB  
Article
Myocardial Strain Assessment for Early Duchenne Muscular Dystrophy Diagnosis in Pediatric Patients Using Cardiac MRI
by Rania Awadi, Narjes Benameur, Hassen Hafsi, Thouraya Ben Younes, Younes Arous, Salam Labidi and João Manuel R. S. Tavares
Appl. Sci. 2024, 14(22), 10341; https://doi.org/10.3390/app142210341 - 11 Nov 2024
Viewed by 469
Abstract
Assessing myocardial strain remains challenging, particularly in the pediatric population, due to the smaller heart sizes, higher heart rates, and variability in strain parameters compared to adult populations. This study aimed to investigate the utility of myocardial strain measurements using cardiac magnetic resonance-feature [...] Read more.
Assessing myocardial strain remains challenging, particularly in the pediatric population, due to the smaller heart sizes, higher heart rates, and variability in strain parameters compared to adult populations. This study aimed to investigate the utility of myocardial strain measurements using cardiac magnetic resonance-feature tracking (CMR-FT) for early diagnosis of Duchenne muscular dystrophy (DMD) in pediatric patients. Twenty-eight DMD patients and 20 healthy controls were involved in this study. Global circumferential, longitudinal, and radial strain (GCS, GLS, and GRS) were measured for the left ventricle (LV) using CMR-FT. Segmental strain values only of the inferolateral and anterolateral LV segments in DMD patients without late gadolinium enhancement (LGE) and DMD patients with LGE were compared to the healthy controls. Strain measurements using CMR-FT in DMD patients were considerably lower than those of healthy controls, with all p-values lower than 0.001. DMD patients without LGE showed decreased inferolateral and anterolateral segmental values only relative to healthy controls. The same behavior was maintained for the LV geometry. Multivariable linear regression demonstrated that the end-systole (ES) wall thicknesses and thickening were associated with decreased GCS and GLS. CMR-FT is crucial in detecting cardiac abnormalities in patients with DMD. It represents an innovative imaging biomarker that can detect initial myocardial alterations in DMD cardiomyopathy without relying on gadolinium. Full article
(This article belongs to the Special Issue Diagnosis of Medical Imaging)
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12 pages, 6208 KiB  
Article
Breakthrough and Challenging Application: Mixed Reality-Assisted Intracardiac Surgery
by Franco Marinozzi, Michela Franzò, Sara Bicchierini, Mizar D’Abramo, Wael Saade, Giuseppe Mazzesi and Fabiano Bini
Appl. Sci. 2024, 14(22), 10151; https://doi.org/10.3390/app142210151 - 6 Nov 2024
Viewed by 478
Abstract
Background: While several studies investigate the utility and clinical value of 3D printing in aiding diagnosis, medical education, preoperative planning, and intraoperative guidance of surgical interventions, there is a scarcity of literature regarding concrete applications of mixed reality in the cardiovascular domain due [...] Read more.
Background: While several studies investigate the utility and clinical value of 3D printing in aiding diagnosis, medical education, preoperative planning, and intraoperative guidance of surgical interventions, there is a scarcity of literature regarding concrete applications of mixed reality in the cardiovascular domain due to its nascent stage of study and expansion. This study goes beyond a mere three-dimensional visualization of the cardiac district, aiming to visualize the intracardiac structures within the scope of preoperative planning for cardiac surgery. Methods: The segmentation of the heart was performed through an open-source and a professional software and by applying different procedures. Each anatomical component of the heart, including the aortic valve, was accurately segmented and a 3D model was built to represent the entire heart. Results: Beyond the three-dimensional visualization of the cardiac region, the intracardiac structures were also segmented. A mixed-reality app was implemented with the possibility of exploding the model, interacting with it, and freely sectioning it with a plane. Conclusions: The proposed segmentation methodology allows a segmentation of the valve and the intracardiac structures. Furthermore, the mixed-reality app has confirmed the potential of this technology in diagnostic and preoperative planning, although some limitations should still be overcome. Full article
(This article belongs to the Special Issue Diagnosis of Medical Imaging)
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13 pages, 25642 KiB  
Article
Evidence-Based Investigation of Coronary Calcium Score in Cardiac Computed Tomography
by Jina Shim, Kyuseok Kim and Youngjin Lee
Appl. Sci. 2024, 14(19), 8906; https://doi.org/10.3390/app14198906 - 3 Oct 2024
Viewed by 500
Abstract
This study aimed to verify whether increased body mass index (BMI) increases the noise in computed tomography (CT) images due to heightened effective thickness, impacting calcium scores. Calcium scores were measured in 30 sets of images from normal weight patients. Calcium scores were [...] Read more.
This study aimed to verify whether increased body mass index (BMI) increases the noise in computed tomography (CT) images due to heightened effective thickness, impacting calcium scores. Calcium scores were measured in 30 sets of images from normal weight patients. Calcium scores were also measured in 30 sets of images from hypothetical overweight and obese patients, generated by extracting the noise from overweight and obese patients, respectively, and inserting it into the images of normal weight patients. In addition, a phantom study was performed using three calcium phantoms with intensities below the threshold of 130 Hounsfield units and three calcium phantoms with intensities above this threshold. Calcium scores were measured in the absence and presence of a bolus at the heart level to simulate an obese patient. All calcium scores were measured by three radiologists. In the patient study, the total calcium scores of the hypothetical overweight and hypothetical obese groups were 14.93% (p = 0.014) and 22.19% (p = 0.012) higher than those of the normal weight group. In the phantom study, the total calcium score of the six calcium phantoms without a bolus was 1.61% higher at a tube voltage of 120 kV than at 100 kV, and 12.06% higher at a slice thickness of 1 mm than at 3 mm. The total calcium score of the six calcium phantoms with a bolus was 0.13% higher at a tube voltage of 120 kV than at 100 kV, and 14.76% higher at a slice thickness of 1 mm than at 3 mm. These results can be used as a reference to train automated calcium scoring programs on effective thickness through deep learning to reduce calcium score errors caused by increased BMI. Full article
(This article belongs to the Special Issue Diagnosis of Medical Imaging)
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14 pages, 10700 KiB  
Article
Deep Learning-Based Workflow for Bone Segmentation and 3D Modeling in Cone-Beam CT Orthopedic Imaging
by Eleonora Tiribilli and Leonardo Bocchi
Appl. Sci. 2024, 14(17), 7557; https://doi.org/10.3390/app14177557 - 27 Aug 2024
Viewed by 1382
Abstract
In this study, a deep learning-based workflow designed for the segmentation and 3D modeling of bones in cone beam computed tomography (CBCT) orthopedic imaging is presented. This workflow uses a convolutional neural network (CNN), specifically a U-Net architecture, to perform precise bone segmentation [...] Read more.
In this study, a deep learning-based workflow designed for the segmentation and 3D modeling of bones in cone beam computed tomography (CBCT) orthopedic imaging is presented. This workflow uses a convolutional neural network (CNN), specifically a U-Net architecture, to perform precise bone segmentation even in challenging anatomical regions such as limbs, joints, and extremities, where bone boundaries are less distinct and densities are highly variable. The effectiveness of the proposed workflow was evaluated by comparing the generated 3D models against those obtained through other segmentation methods, including SegNet, binary thresholding, and graph cut algorithms. The accuracy of these models was quantitatively assessed using the Jaccard index, the Dice coefficient, and the Hausdorff distance metrics. The results indicate that the U-Net-based segmentation consistently outperforms other techniques, producing more accurate and reliable 3D bone models. The user interface developed for this workflow facilitates intuitive visualization and manipulation of the 3D models, enhancing the usability and effectiveness of the segmentation process in both clinical and research settings. The findings suggest that the proposed deep learning-based workflow holds significant potential for improving the accuracy of bone segmentation and the quality of 3D models derived from CBCT scans, contributing to better diagnostic and pre-surgical planning outcomes in orthopedic practice. Full article
(This article belongs to the Special Issue Diagnosis of Medical Imaging)
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13 pages, 4566 KiB  
Article
Assessment of Feldkamp-Davis-Kress Reconstruction Parameters in Overall Image Quality in Cone Beam Computed Tomography
by Hajin Kim, Jun-Seon Choi and Youngjin Lee
Appl. Sci. 2024, 14(16), 7058; https://doi.org/10.3390/app14167058 - 12 Aug 2024
Viewed by 1180
Abstract
In low-dose cone beam computed tomography (CT), the insufficient number of photons inevitably results in noise, which reduces the accuracy of disease diagnosis. One approach to improving the image quality of CT images acquired using a low-dose protocol involves the utilization of a [...] Read more.
In low-dose cone beam computed tomography (CT), the insufficient number of photons inevitably results in noise, which reduces the accuracy of disease diagnosis. One approach to improving the image quality of CT images acquired using a low-dose protocol involves the utilization of a reconstruction algorithm that efficiently reduces noise. In this study, we modeled the Feldkamp–Davis–Kress (FDK) algorithm using various filters and projection angles and applied it to the reconstruction process using CT simulation. To quantitatively evaluate the quality of the reconstruction images, we measured the coefficient of variation (COV), and signal-to-noise ratio (SNR) in the air, brain, and bone regions to evaluate the noise level. Furthermore, we calculated root mean square error (RMSE), universal image quality index (UQI), and blind/referenceless image spatial quality evaluator (BRISQUE) as similarity and no-reference evaluation. The Hann filter of the FDK algorithm showed superior performance in terms of COV, SNR, RMSE, and UQI compared to the other filters. In addition, when analyzing the COV and SNR results, we observed that image quality increased significantly at projection angles smaller than approximately 2.8°. Moreover, based on BRISQUE results, we confirm that the Shepp–Logan filter exhibited the most superior performance. In conclusion, we believe that the application of the Hann filter in the FDK reconstruction process offers significant advantages in improving the image quality acquired under a low-dose protocol, and we expect that our study will be a preliminary study of no-reference evaluation of CT reconstruction images. Full article
(This article belongs to the Special Issue Diagnosis of Medical Imaging)
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12 pages, 3132 KiB  
Article
Intraoperative PRO Score Assessment of Actinic Keratosis with FCF Fast Green-Enhanced Ex Vivo Confocal Microscopy
by Daniela Hartmann, Lisa Buttgereit, Lara Stärr, Elke Christina Sattler, Lars Einar French and Maximilian Deußing
Appl. Sci. 2024, 14(3), 1150; https://doi.org/10.3390/app14031150 - 30 Jan 2024
Cited by 2 | Viewed by 1231
Abstract
Actinic keratoses (AKs) represent a common skin cancer in situ associated with chronic sun exposure. Early diagnosis and management of AKs are crucial to prevent their progression to invasive squamous cell carcinoma. Therefore, we investigated AK PRO score assessment using ex vivo confocal [...] Read more.
Actinic keratoses (AKs) represent a common skin cancer in situ associated with chronic sun exposure. Early diagnosis and management of AKs are crucial to prevent their progression to invasive squamous cell carcinoma. Therefore, we investigated AK PRO score assessment using ex vivo confocal laser microscopy (EVCM) coupled with a novel fluorescent dye, FCF Fast Green, to explore its potential for the precise imaging and discrimination of collagen fibers. AK PRO assessment using EVCM demonstrated excellent conformity (95.8%) with histopathologic examination. The additional utilization of FCF Fast Green dye had no impact on AK visualization but showed a high affinity for collagen fibers enabling clear differentiation of collagen alterations between healthy and sun-damaged skin. The enhanced visualization of collagen fiber changes may aid clinicians in accurately identifying AKs and differentiating them from benign skin lesions. Full article
(This article belongs to the Special Issue Diagnosis of Medical Imaging)
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Review

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25 pages, 3942 KiB  
Review
Non-Surgical Treatment for Hepatocellular Carcinoma: What to Expect at Follow-Up Magnetic Resonance Imaging—A Pictorial Review
by Andreea-Elena Scheau, Sandra Oana Jurca, Cristian Scheau and Ioana Gabriela Lupescu
Appl. Sci. 2024, 14(20), 9159; https://doi.org/10.3390/app14209159 - 10 Oct 2024
Viewed by 1166
Abstract
Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, represents a significant global health challenge due to its rising incidence, complex management, as well as recurrence rates of up to 70% or more. Early and accurate imaging diagnosis, through modalities such as [...] Read more.
Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, represents a significant global health challenge due to its rising incidence, complex management, as well as recurrence rates of up to 70% or more. Early and accurate imaging diagnosis, through modalities such as ultrasound, CT, and MRI, is crucial for effective treatment. Minimally invasive therapies, including thermal ablation methods such as radiofrequency ablation, microwave ablation, laser ablation, high-intensity focused ultrasound, and cryoablation, as well as non-thermal methods like percutaneous ethanol injection and irreversible electroporation, have shown promise in treating early and intermediate stages of HCC. Some studies have reported complete response in more than 90% of nodules and survival rates of up to 60–85% at 5 years after the procedure. These therapies are increasingly employed and induce specific morphological and physiological changes in the tumor and surrounding liver tissue, which are critical to monitor for assessing treatment efficacy and detecting recurrence. This review highlights the imaging characteristics of HCC following non-surgical treatments, focusing on the common features, challenges in post-treatment evaluation, and the importance of standardized imaging protocols such as the Liver Imaging Reporting and Data System. Understanding these imaging features is essential for radiologists to accurately assess tumor viability and guide further therapeutic decisions, ultimately improving patient outcomes. Full article
(This article belongs to the Special Issue Diagnosis of Medical Imaging)
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