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Applied Sciences
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Latest Approaches for Medical Image Analysis
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Latest Approaches for Medical Image Analysis

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 6254

Special Issue Editors

Prof. Dr. Il Dong Yun

E-Mail Website
Guest Editor
Division of computer and electronic systems, Hankuk University of Foreign Studies, Seoul 02450, Republic of Korea
Interests: medical image analysis; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Walid Abdullah Al

E-Mail Website
Guest Editor
Division of Computer Engineering, Hankuk University of Foreign Studies, Seoul 02450, Republic of Korea
Interests: medical image analysis; computer vision; reinforment learning

Special Issue Information

Dear Colleagues,

Medical imaging is a broad field that involves image capture for diagnostic and therapeutic purposes. Processing and analyzing medical images are often used to visualize anatomy or assess the function of human organs, pinpoint areas of pathology, analyze biological and metabolic processes, develop treatment plans, and perform image-guided surgery. This powerful tool brings much-needed quantitative information, providing enhanced medical information to benefit patients without increasing the already high cost of healthcare. In addition, the large number of applications that rely on this technology has increased the need for efficient, accurate, and reliable algorithms for medical image processing and analysis, especially as imaging techniques become increasingly complex and the number of images that need to be processed increases. Deep learning, especially supervised learning using convolutional neural networks for classification or segmentation, has recently been fully embraced and established by most medical professionals as a necessary technique to aid clinical procedures. Recent advances in machine learning and computer vision, including new work on semi-supervised learning, weakly supervised learning, transfer learning, and unsupervised learning, may help address many problems, such as reducing the cost of collecting data annotations. New neural network structures such as Figure Neural Networks and improved CNN structures from Neural Architecture Search are also proposed to improve inference ability given the same data.

The main topic of this issue will be medical image analysis, with a focus on its recent applications and developments in the clinic. We also believe that there are many obstacles and problems for medical image analysis technology to revolutionize clinical practice.

Prof. Dr. Il Dong Yun
Dr. Walid Abdullah Al
Guest Editors

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Published Papers (4 papers)

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Research

18 pages, 8292 KiB  
Article
Cycle Consistent Generative Motion Artifact Correction in Coronary Computed Tomography Angiography
by Amal Muhammad Saleem, Sunghee Jung, Hyuk-Jae Chang and Soochahn Lee
Appl. Sci. 2024, 14(5), 1859; https://doi.org/10.3390/app14051859 - 23 Feb 2024
Viewed by 1039
Abstract
In coronary computed tomography angiography (CCTA), motion artifacts due to heartbeats can obscure coronary artery diagnoses. In this study, we introduce a cycle-consistent adversarial-network-based method for motion artifact correction in CCTA. Our methodology involves extracting image patches and using style transfer for synthetic [...] Read more.
In coronary computed tomography angiography (CCTA), motion artifacts due to heartbeats can obscure coronary artery diagnoses. In this study, we introduce a cycle-consistent adversarial-network-based method for motion artifact correction in CCTA. Our methodology involves extracting image patches and using style transfer for synthetic ground truth creation, followed by CycleGAN network training for motion compensation. We employ Dynamic Time Warping (DTW) to align extracted image patches along the artery centerline with their corresponding motion-free phase patches, ensuring matched pixel correspondences and similar anatomical features for accuracy in subsequent processing steps. Our quantitative analysis, using metrics like the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), demonstrates CycleGAN’s superior performance in reducing motion artifacts, with improvements in image quality and clarity. An observer study using a 5-point Likert scale further validates the reduction of motion artifacts and improved visibility of coronary arteries. Additionally, we present a quantitative analysis on clinical data, affirming the correction of motion artifacts through metric-based evaluations. Full article
(This article belongs to the Special Issue Latest Approaches for Medical Image Analysis)
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12 pages, 892 KiB  
Communication
Automatic Needle Route Proposal in Preoperative Neck CT for Injection Laryngoplasty
by Walid Abdullah Al, Wonjae Cha and Il Dong Yun
Appl. Sci. 2023, 13(18), 10554; https://doi.org/10.3390/app131810554 - 21 Sep 2023
Viewed by 1263
Abstract
Transcutaneous injection laryngoplasty (TIL) is a commonly used method to treat vocal fold paresis, where the affected vocal folds are augmented through injection. Determining the injection site and route is a major step during the preprocedural planning of TIL. In this communication, we [...] Read more.
Transcutaneous injection laryngoplasty (TIL) is a commonly used method to treat vocal fold paresis, where the affected vocal folds are augmented through injection. Determining the injection site and route is a major step during the preprocedural planning of TIL. In this communication, we propose and investigate an automatic method for needle route computation in preoperative neck CT. Recently, deep reinforcement learning (RL) agents showed noteworthy results for localizing the vocal folds. In this work, we focus on finding the optimal needle trajectory from the neck skin to the vocal folds localized by such RL agents. Identifying critical structures and constraints in the medical routine, we propose a minimal cost-based search to find the optimal path. Furthermore, we evaluate the proposed method with neck CT volumes from 136 patients, where it is shown that our computed needle paths have high accuracy. Full article
(This article belongs to the Special Issue Latest Approaches for Medical Image Analysis)
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19 pages, 3379 KiB  
Article
A Novel Deep Dense Block-Based Model for Detecting Alzheimer’s Disease
by Selahattin Barış Çelebi and Bülent Gürsel Emiroğlu
Appl. Sci. 2023, 13(15), 8686; https://doi.org/10.3390/app13158686 - 27 Jul 2023
Cited by 6 | Viewed by 1451
Abstract
Alzheimer’s disease (AD), the most common form of dementia and neurological disorder, affects a significant number of elderly people worldwide. The main objective of this study was to develop an effective method for quickly diagnosing healthy individuals (CN) before they progress to mild [...] Read more.
Alzheimer’s disease (AD), the most common form of dementia and neurological disorder, affects a significant number of elderly people worldwide. The main objective of this study was to develop an effective method for quickly diagnosing healthy individuals (CN) before they progress to mild cognitive impairment (MCI). Moreover, this study presents a unique approach to decomposing AD into stages using machine-learning architectures with the help of tensor-based morphometric image analysis. The proposed model, which uses a neural network built on the Xception architecture, was thoroughly assessed by comparing it with the most recent convolutional neural network (CNN) models described in the literature. The proposed method outperformed the other models in terms of performance, achieving an impressive average classification accuracy of 95.81% using the dataset. It also had very high sensitivity, specificity, accuracy, and F1 scores, with average scores of 95.41%, 97.92%, 95.01%, and 95.21%, respectively. In addition, it showed a superior classification ability compared to alternative methods, especially for MCI estimation, as evidenced by a mean area under the ROC curve (AUC) of 0.97. Our study demonstrated the effectiveness of deep-learning-based morphometric analysis using brain images for early AD diagnosis. Full article
(This article belongs to the Special Issue Latest Approaches for Medical Image Analysis)
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21 pages, 3082 KiB  
Article
A Visual Analytics Framework for Inter-Hospital Transfer Network of Stroke Patients
by Kyuhan Kwak, Jinu Park and Hyunjoo Song
Appl. Sci. 2023, 13(9), 5241; https://doi.org/10.3390/app13095241 - 22 Apr 2023
Viewed by 1704
Abstract
Effective inter-hospital coordination is crucial in improving the stroke treatment process and outcomes. The introduction of endovascular thrombectomy (EVT) further emphasized the importance of coordination. Although previous studies considered various clinical data besides stroke in terms of the network structure between hospitals, a [...] Read more.
Effective inter-hospital coordination is crucial in improving the stroke treatment process and outcomes. The introduction of endovascular thrombectomy (EVT) further emphasized the importance of coordination. Although previous studies considered various clinical data besides stroke in terms of the network structure between hospitals, a majority of these studies performed only quantitative analyses instead of topological analyses. This study proposes a new framework (PatientFlow) for constructing a network based on stroke patient transfer data and performing exploratory analysis. The proposed framework can visualize the network structure among hospitals at the national level and analyze the detailed structure through dynamic queries. The hub-and-spoke structure for each cluster derived through community detection can be compared visually and analyzed quantitatively using network measures. Further, the relationship between regions can be analyzed by aggregating the transfer of patients by province. PatientFlow allows medical researchers to perform an exploratory analysis to understand the network at the national, provincial, and community levels with multiple coordinated views. Full article
(This article belongs to the Special Issue Latest Approaches for Medical Image Analysis)
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