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Computation
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Computational Medical Image Analysis—2nd Edition
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Computational Medical Image Analysis—2nd Edition

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Biology".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 5735

Special Issue Editor

Dr. Anando Sen

E-Mail Website
Guest Editor
John Walton Muscular Dystrophy Research Centre, Translational and Clinical Research Institute, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne NE1 3BZ, UK
Interests: medical imaging; mathematical modelling; image quality; pathology correlation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is my great pleasure to invite you to contribute to this Special Issue of Computation, titled “Computational Medical Image Analysis—2nd Edition”. It is devoted to understanding the modern methodologies used in a variety of medical imaging applications. Colleagues from all over the world are invited to submit their manuscripts. These papers will follow a rigorous peer-review process to satisfy a high standard of publication.

Computational methods are extensively used in medical image analysis. With the development of high-performance systems as well as methodologies that can harness the power of these systems (e.g., machine learning and deep learning), this is an exciting era for imaging research. With novel methodologies, it has been possible to provide previously unfathomable solutions to important problems. In this Special Issue, we hope to put together a collection of such methods.

The scope of this Special Issue is vast. The application must be clinically relevant and patient-oriented. The use of both synthetic and real data is acceptable. Applications from a diverse range of imaging modalities, including CT, MR, SPECT, PET, ultrasound, photoacoustic, and digital pathology, are encouraged. Topics for this Special Issue include, but are not limited to, the following:

  • Image processing;
  • Dual and multi-modality imaging;
  • Image segmentation;
  • Image registration;
  • Tomographic reconstruction;
  • Image quality assessment;
  • Digital pathology applications;
  • Dosimetry;
  • Radiation oncology applications;
  • Machine learning;
  • Neural networks.

Dr. Anando Sen
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. Computation 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 1800 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

  • computational imaging
  • medical imaging
  • anatomical imaging
  • functional imaging
  • machine learning
  • neural networks
  • digital pathology

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Related Special Issue

Published Papers (5 papers)

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Research

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22 pages, 10018 KiB  
Article
Eye Care: Predicting Eye Diseases Using Deep Learning Based on Retinal Images
by Araek Tashkandi
Computation 2025, 13(4), 91; https://doi.org/10.3390/computation13040091 - 3 Apr 2025
Viewed by 496
Abstract
Eye illness detection is important, yet it can be difficult and error-prone. In order to effectively and promptly diagnose eye problems, doctors must use cutting-edge technologies. The goal of this research paper is to develop a sophisticated model that will help physicians detect [...] Read more.
Eye illness detection is important, yet it can be difficult and error-prone. In order to effectively and promptly diagnose eye problems, doctors must use cutting-edge technologies. The goal of this research paper is to develop a sophisticated model that will help physicians detect different eye conditions early on. These conditions include age-related macular degeneration (AMD), diabetic retinopathy, cataracts, myopia, and glaucoma. Common eye conditions include cataracts, which cloud the lens and cause blurred vision, and glaucoma, which can cause vision loss due to damage to the optic nerve. The two conditions that could cause blindness if treatment is not received are age-related macular degeneration (AMD) and diabetic retinopathy, a side effect of diabetes that destroys the blood vessels in the retina. Problems include myopic macular degeneration, glaucoma, and retinal detachment—severe types of nearsightedness that are typically defined as having a refractive error of –5 diopters or higher—are also more likely to occur in people with high myopia. We intend to apply a user-friendly approach that will allow for faster and more efficient examinations. Our research attempts to streamline the eye examination procedure, making it simpler and more accessible than traditional hospital approaches. Our goal is to use deep learning and machine learning to develop an extremely accurate model that can assess medical images, such as eye retinal scans. This was accomplished by using a huge dataset to train the machine learning and deep learning model, as well as sophisticated image processing techniques to assist the algorithm in identifying patterns of various eye illnesses. Following training, we discovered that the CNN, VggNet, MobileNet, and hybrid Deep Learning models outperformed the SVM and Random Forest machine learning models in terms of accuracy, achieving above 98%. Therefore, our model could assist physicians in enhancing patient outcomes, raising survival rates, and creating more effective treatment plans for patients with these illnesses. Full article
(This article belongs to the Special Issue Computational Medical Image Analysis—2nd Edition)
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15 pages, 766 KiB  
Article
MedMAE: A Self-Supervised Backbone for Medical Imaging Tasks
by Anubhav Gupta, Islam Osman, Mohamed S. Shehata, W. John Braun and Rebecca E. Feldman
Computation 2025, 13(4), 88; https://doi.org/10.3390/computation13040088 - 1 Apr 2025
Viewed by 373
Abstract
Medical imaging tasks are very challenging due to the lack of publicly available labeled datasets. Hence, it is difficult to achieve high performance with existing deep learning models as they require a massive labeled dataset to be trained effectively. An alternative solution is [...] Read more.
Medical imaging tasks are very challenging due to the lack of publicly available labeled datasets. Hence, it is difficult to achieve high performance with existing deep learning models as they require a massive labeled dataset to be trained effectively. An alternative solution is to use pre-trained models and fine-tune them using a medical imaging dataset. However, all existing models are pre-trained using natural images, which represent a different domain from that of medical imaging; this leads to poor performance due to domain shift. To overcome these problems, we propose a pre-trained backbone using a collected medical imaging dataset with a self-supervised learning tool called a masked autoencoder. This backbone can be used as a pre-trained model for any medical imaging task, as it is trained to learn a visual representation of different types of medical images. To evaluate the performance of the proposed backbone, we use four different medical imaging tasks. The results are compared with existing pre-trained models. These experiments show the superiority of our proposed backbone in medical imaging tasks. Full article
(This article belongs to the Special Issue Computational Medical Image Analysis—2nd Edition)
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22 pages, 13050 KiB  
Article
A Deep Learning Model for Detecting Fake Medical Images to Mitigate Financial Insurance Fraud
by Muhammad Asad Arshed, Shahzad Mumtaz, Ștefan Cristian Gherghina, Neelam Urooj, Saeed Ahmed and Christine Dewi
Computation 2024, 12(9), 173; https://doi.org/10.3390/computation12090173 - 29 Aug 2024
Viewed by 2673
Abstract
Artificial Intelligence and Deepfake Technologies have brought a new dimension to the generation of fake data, making it easier and faster than ever before—this fake data could include text, images, sounds, videos, etc. This has brought new challenges that require the faster development [...] Read more.
Artificial Intelligence and Deepfake Technologies have brought a new dimension to the generation of fake data, making it easier and faster than ever before—this fake data could include text, images, sounds, videos, etc. This has brought new challenges that require the faster development of tools and techniques to avoid fraudulent activities at pace and scale. Our focus in this research study is to empirically evaluate the use and effectiveness of deep learning models such as Convolutional Neural Networks (CNNs) and Patch-based Neural Networks in the context of successful identification of real and fake images. We chose the healthcare domain as a potential case study where the fake medical data generation approach could be used to make false insurance claims. For this purpose, we obtained publicly available skin cancer data and used recently introduced stable diffusion approaches—a more effective technique than prior approaches such as Generative Adversarial Network (GAN)—to generate fake skin cancer images. To the best of our knowledge, and based on the literature review, this is one of the few research studies that uses images generated using stable diffusion along with real image data. As part of the exploratory analysis, we analyzed histograms of fake and real images using individual color channels and averaged across training and testing datasets. The histogram analysis demonstrated a clear change by shifting the mean and overall distribution of both real and fake images (more prominent in blue and green) in the training data whereas, in the test data, both means were different from the training data, so it appears to be non-trivial to set a threshold which could give better predictive capability. We also conducted a user study to observe where the naked eye could identify any patterns for classifying real and fake images, and the accuracy of the test data was observed to be 68%. The adoption of deep learning predictive approaches (i.e., patch-based and CNN-based) has demonstrated similar accuracy (~100%) in training and validation subsets of the data, and the same was observed for the test subset with and without StratifiedKFold (k = 3). Our analysis has demonstrated that state-of-the-art exploratory and deep-learning approaches are effective enough to detect images generated from stable diffusion vs. real images. Full article
(This article belongs to the Special Issue Computational Medical Image Analysis—2nd Edition)
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9 pages, 5096 KiB  
Article
Ultrashort Echo Time and Fast Field Echo Imaging for Spine Bone Imaging with Application in Spondylolysis Evaluation
by Diana Vucevic, Vadim Malis, Yuichi Yamashita, Anya Mesa, Tomosuke Yamaguchi, Suraj Achar, Mitsue Miyazaki and Won C. Bae
Computation 2024, 12(8), 152; https://doi.org/10.3390/computation12080152 - 24 Jul 2024
Cited by 1 | Viewed by 1340
Abstract
Isthmic spondylolysis is characterized by a stress injury to the pars interarticularis bones of the lumbar spines and is often missed by conventional magnetic resonance imaging (MRI), necessitating a computed tomography (CT) for accurate diagnosis. We compare MRI techniques suitable for producing CT-like [...] Read more.
Isthmic spondylolysis is characterized by a stress injury to the pars interarticularis bones of the lumbar spines and is often missed by conventional magnetic resonance imaging (MRI), necessitating a computed tomography (CT) for accurate diagnosis. We compare MRI techniques suitable for producing CT-like images. Lumbar spines of asymptomatic and low back pain (LBP) subjects were imaged at 3-Tesla with multi-echo ultrashort echo time (UTE) and field echo (FE) sequences followed by simple post-processing of averaging and inverting to depict spinal bones with a CT-like appearance. The contrast-to-noise ratio (CNR) for bone was determined to compare UTE vs. FE and single-echo vs. multi-echo data. Visually, both sequences depicted cortical bone with good contrast; UTE-processed sequences provided a flatter contrast for soft tissues that made them easy to distinguish from bone, while FE-processed images had better resolution and bone–muscle contrast, which are important for fracture detection. Additionally, multi-echo images provided significantly (p = 0.03) greater CNR compared with single-echo images. Using these techniques, progressive spondylolysis was detected in an LBP subject. This study demonstrates the feasibility of using spine bone MRI to yield CT-like contrast. Through the employment of multi-echo UTE and FE sequences combined with simple processing, we observe sufficient enhancements in image quality and contrast to detect pars fractures. Full article
(This article belongs to the Special Issue Computational Medical Image Analysis—2nd Edition)
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Review

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20 pages, 2405 KiB  
Review
A Bibliometric Review of Deep Learning Approaches in Skin Cancer Research
by Catur Supriyanto, Abu Salam, Junta Zeniarja, Danang Wahyu Utomo, Ika Novita Dewi, Cinantya Paramita, Adi Wijaya and Noor Zuraidin Mohd Safar
Computation 2025, 13(3), 78; https://doi.org/10.3390/computation13030078 - 19 Mar 2025
Viewed by 551
Abstract
Early detection of skin cancer is crucial for successful treatment and improved patient outcomes. Medical images play a vital role in this process, serving as the primary data source for both traditional and modern diagnostic approaches. This study aims to provide an overview [...] Read more.
Early detection of skin cancer is crucial for successful treatment and improved patient outcomes. Medical images play a vital role in this process, serving as the primary data source for both traditional and modern diagnostic approaches. This study aims to provide an overview of the significant role of medical images in skin cancer detection and highlight developments in the use of deep learning for early diagnosis. The scope of this survey includes an in-depth exploration of state-of-the-art deep learning methods, an evaluation of public datasets commonly used for training and validation, and a bibliometric analysis of recent advancements in the field. This survey focuses on publications in the Scopus database from 2019 to 2024. The search string is used to find articles by their abstracts, titles, and keywords, and includes several public datasets, like HAM and ISIC, ensuring relevance to the topic. Filters are applied based on the year, document type, source type, and language. The analysis identified 1697 articles, predominantly comprising journal articles and conference proceedings. The analysis shows that the number of articles has increased over the past five years. This growth is driven not only by developed countries but also by developing countries. Dermatology departments in various hospitals play a significant role in advancing skin cancer detection methods. In addition to identifying publication trends, this study also reveals underexplored areas to encourage new explorations using the VOSviewer and Bibliometrix applications. Full article
(This article belongs to the Special Issue Computational Medical Image Analysis—2nd Edition)
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